diff --git a/HOW_TO_RELEASE.md b/HOW_TO_RELEASE.md
index fdfc60719..237d7e414 100644
--- a/HOW_TO_RELEASE.md
+++ b/HOW_TO_RELEASE.md
@@ -42,15 +42,16 @@
 
 00. Run black to ensure that the codebase passes all style checks.
     This check should only take a few seconds.  These checks are also done on
-    Travis and are platform independent, so they should not be necessary to
+    GitHub Actions and are platform independent, so they should not be necessary to
     replicate locally, but are listed here for completeness.
     ```sh
     black --check --diff .
     ```
 
-00. Run the regular test suite on Windows. Travis tests are done on Linux,
-    but most users are on Windows, and the test suite should also be run
-    on Windows to ensure that it works on that platform as well.  If you
+00. Run the regular test suite on Windows. Most GitHub Actions tests are done on Linux,
+    Linux (it's faster to start up and run a new clean VM for testing) but most
+    users are on Windows, and the test suite should also be run on Windows to
+    ensure that it works on that platform as well.  If you
     are not preparing this release on Windows, you should be sure to run
     at least through this step on a Windows machine before finalizing a
     release.
@@ -135,6 +136,10 @@
     ```sh
     gh release create v1.2.3
     ```
+    The process of creating and tagging a release will automatically
+    trigger various GitHub Actions scripts to build, test, and publish the
+    new release to PyPI and conda forge, assuming there are no errors.
+
     For a development pre-release, include the `--prerelease` argument.
     As the project's policy is that only formally released code is merged
     to the main branch, any pre-release should also be built against a
diff --git a/activitysim/abm/models/__init__.py b/activitysim/abm/models/__init__.py
index 91b802d76..1c288f8c4 100644
--- a/activitysim/abm/models/__init__.py
+++ b/activitysim/abm/models/__init__.py
@@ -20,6 +20,7 @@
     joint_tour_frequency,
     joint_tour_participation,
     joint_tour_scheduling,
+    joint_tour_frequency_composition,
     location_choice,
     mandatory_scheduling,
     mandatory_tour_frequency,
diff --git a/activitysim/abm/models/cdap.py b/activitysim/abm/models/cdap.py
index b37cf8a9a..f7da93687 100644
--- a/activitysim/abm/models/cdap.py
+++ b/activitysim/abm/models/cdap.py
@@ -78,6 +78,17 @@ def cdap_simulate(persons_merged, persons, households, chunk_size, trace_hh_id):
         file_name=model_settings["FIXED_RELATIVE_PROPORTIONS_SPEC"]
     )
 
+    add_joint_tour_utility = model_settings.get("ADD_JOINT_TOUR_UTILITY", False)
+
+    if add_joint_tour_utility:
+        # Rules and coefficients for generating cdap joint tour specs for different household sizes
+        joint_tour_coefficients_file_name = model_settings.get(
+            "JOINT_TOUR_COEFFICIENTS", "cdap_joint_tour_coefficients.csv"
+        )
+        cdap_joint_tour_coefficients = pd.read_csv(
+            config.config_file_path(joint_tour_coefficients_file_name), comment="#"
+        )
+
     persons_merged = persons_merged.to_frame()
 
     # add tour-based chunk_id so we can chunk all trips in tour together
@@ -101,11 +112,27 @@ def cdap_simulate(persons_merged, persons, households, chunk_size, trace_hh_id):
     # (also when multiprocessing locutor might not see all household sizes)
     logger.info("Pre-building cdap specs")
     for hhsize in range(2, cdap.MAX_HHSIZE + 1):
-        spec = cdap.build_cdap_spec(cdap_interaction_coefficients, hhsize, cache=True)
+        spec = cdap.build_cdap_spec(
+            cdap_interaction_coefficients,
+            hhsize,
+            cache=True,
+            joint_tour_alt=add_joint_tour_utility,
+        )
         if inject.get_injectable("locutor", False):
             spec.to_csv(
                 config.output_file_path("cdap_spec_%s.csv" % hhsize), index=True
             )
+        if add_joint_tour_utility:
+            # build cdap joint tour spec
+            # joint_spec_dependency = spec.loc[[c for c in spec.index if c.startswith(('M_p', 'N_p', 'H_p'))]]
+            joint_spec = cdap.build_cdap_joint_spec(
+                cdap_joint_tour_coefficients, hhsize, cache=True
+            )
+            if inject.get_injectable("locutor", False):
+                joint_spec.to_csv(
+                    config.output_file_path("cdap_joint_spec_%s.csv" % hhsize),
+                    index=True,
+                )
 
     if estimator:
         estimator.write_model_settings(model_settings, "cdap.yaml")
@@ -127,17 +154,32 @@ def cdap_simulate(persons_merged, persons, households, chunk_size, trace_hh_id):
 
     logger.info("Running cdap_simulate with %d persons", len(persons_merged.index))
 
-    choices = cdap.run_cdap(
-        persons=persons_merged,
-        person_type_map=person_type_map,
-        cdap_indiv_spec=cdap_indiv_spec,
-        cdap_interaction_coefficients=cdap_interaction_coefficients,
-        cdap_fixed_relative_proportions=cdap_fixed_relative_proportions,
-        locals_d=constants,
-        chunk_size=chunk_size,
-        trace_hh_id=trace_hh_id,
-        trace_label=trace_label,
-    )
+    if add_joint_tour_utility:
+        choices, hh_joint = cdap.run_cdap(
+            persons=persons_merged,
+            person_type_map=person_type_map,
+            cdap_indiv_spec=cdap_indiv_spec,
+            cdap_interaction_coefficients=cdap_interaction_coefficients,
+            cdap_fixed_relative_proportions=cdap_fixed_relative_proportions,
+            locals_d=constants,
+            chunk_size=chunk_size,
+            trace_hh_id=trace_hh_id,
+            trace_label=trace_label,
+            add_joint_tour_utility=add_joint_tour_utility,
+        )
+    else:
+        choices = cdap.run_cdap(
+            persons=persons_merged,
+            person_type_map=person_type_map,
+            cdap_indiv_spec=cdap_indiv_spec,
+            cdap_interaction_coefficients=cdap_interaction_coefficients,
+            cdap_fixed_relative_proportions=cdap_fixed_relative_proportions,
+            locals_d=constants,
+            chunk_size=chunk_size,
+            trace_hh_id=trace_hh_id,
+            trace_label=trace_label,
+            add_joint_tour_utility=add_joint_tour_utility,
+        )
 
     if estimator:
         estimator.write_choices(choices)
@@ -161,6 +203,11 @@ def cdap_simulate(persons_merged, persons, households, chunk_size, trace_hh_id):
 
     # - annotate households table
     households = households.to_frame()
+
+    if add_joint_tour_utility:
+        hh_joint = hh_joint.reindex(households.index)
+        households["has_joint_tour"] = hh_joint
+
     expressions.assign_columns(
         df=households,
         model_settings=model_settings.get("annotate_households"),
diff --git a/activitysim/abm/models/joint_tour_frequency_composition.py b/activitysim/abm/models/joint_tour_frequency_composition.py
new file mode 100644
index 000000000..3e52d27fa
--- /dev/null
+++ b/activitysim/abm/models/joint_tour_frequency_composition.py
@@ -0,0 +1,204 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+import logging
+
+import numpy as np
+import pandas as pd
+import os
+from activitysim.core.interaction_simulate import interaction_simulate
+
+from activitysim.core import simulate
+from activitysim.core import tracing
+from activitysim.core import pipeline
+from activitysim.core import config
+from activitysim.core import inject
+from activitysim.core import expressions
+
+from .util import estimation
+
+from .util.overlap import hh_time_window_overlap
+from .util.tour_frequency import process_joint_tours_frequency_composition
+
+logger = logging.getLogger(__name__)
+
+
+@inject.step()
+def joint_tour_frequency_composition(
+    households_merged, persons, chunk_size, trace_hh_id
+):
+    """
+    This model predicts the frequency and composition of fully joint tours.
+    """
+
+    trace_label = "joint_tour_frequency_composition"
+    model_settings_file_name = "joint_tour_frequency_composition.yaml"
+
+    model_settings = config.read_model_settings(model_settings_file_name)
+
+    alt_tdd = simulate.read_model_alts(
+        "joint_tour_frequency_composition_alternatives.csv", set_index="alt"
+    )
+
+    # - only interested in households with more than one cdap travel_active person and
+    # - at least one non-preschooler
+    households_merged = households_merged.to_frame()
+    choosers = households_merged[households_merged.participates_in_jtf_model].copy()
+
+    # - only interested in persons in choosers households
+    persons = persons.to_frame()
+    persons = persons[persons.household_id.isin(choosers.index)]
+
+    logger.info("Running %s with %d households", trace_label, len(choosers))
+
+    # alt preprocessor
+    alt_preprocessor_settings = model_settings.get("ALTS_PREPROCESSOR", None)
+    if alt_preprocessor_settings:
+
+        locals_dict = {}
+
+        alt_tdd = alt_tdd.copy()
+
+        expressions.assign_columns(
+            df=alt_tdd,
+            model_settings=alt_preprocessor_settings,
+            locals_dict=locals_dict,
+            trace_label=trace_label,
+        )
+
+    # - preprocessor
+    preprocessor_settings = model_settings.get("preprocessor", None)
+    if preprocessor_settings:
+
+        locals_dict = {
+            "persons": persons,
+            "hh_time_window_overlap": hh_time_window_overlap,
+        }
+
+        expressions.assign_columns(
+            df=choosers,
+            model_settings=preprocessor_settings,
+            locals_dict=locals_dict,
+            trace_label=trace_label,
+        )
+
+    estimator = estimation.manager.begin_estimation("joint_tour_frequency_composition")
+
+    model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"])
+    coefficients_df = simulate.read_model_coefficients(model_settings)
+    model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator)
+
+    constants = config.get_model_constants(model_settings)
+
+    if estimator:
+        estimator.write_spec(model_settings)
+        estimator.write_model_settings(model_settings, model_settings_file_name)
+        estimator.write_coefficients(coefficients_df, model_settings)
+        estimator.write_choosers(choosers)
+        estimator.write_alternatives(alts)
+
+        assert choosers.index.name == "household_id"
+        assert "household_id" not in choosers.columns
+        choosers["household_id"] = choosers.index
+
+        estimator.set_chooser_id(choosers.index.name)
+
+    # The choice value 'joint_tour_frequency_composition' assigned by interaction_simulate
+    # is the index value of the chosen alternative in the alternatives table.
+    choices = interaction_simulate(
+        choosers=choosers,
+        alternatives=alt_tdd,
+        spec=model_spec,
+        locals_d=constants,
+        chunk_size=chunk_size,
+        trace_label=trace_label,
+        trace_choice_name=trace_label,
+        estimator=estimator,
+    )
+
+    if estimator:
+        estimator.write_choices(choices)
+        choices = estimator.get_survey_values(
+            choices, "households", "joint_tour_frequency_composition"
+        )
+        estimator.write_override_choices(choices)
+        estimator.end_estimation()
+
+    # add joint tour frequency composition column to household table
+    households_merged["joint_tour_frequency_composition"] = choices.reindex(
+        households_merged.index
+    ).fillna(0)
+
+    # - create joint_tours based on choices
+
+    # - we need a person_id in order to generate the tour index (and for register_traceable_table)
+    # - but we don't know the tour participants yet
+    # - so we arbitrarily choose the first person in the household
+    # - to be point person for the purpose of generating an index and setting origin
+    temp_point_persons = persons.loc[persons.PNUM == 1]
+    temp_point_persons["person_id"] = temp_point_persons.index
+    temp_point_persons = temp_point_persons.set_index("household_id")
+    temp_point_persons = temp_point_persons[["person_id", "home_zone_id"]]
+
+    # create a tours table of tour_category "joint" and different tour_types (e.g. shopping, eat)
+    # and add the composition column (adults or children or mixed) to the tour
+
+    # Choices
+    # hhid	choice
+    # 11111	1
+    # 22222	2
+    # 33333	3
+
+    # Alts
+    # alt	purpose1	purpose2	party1	party2	eat	shop
+    # 1	    5	        0	        3	    0	    1	0
+    # 2	    5	        6	        1	    3	    1	1
+    # 3	    6	        0	        1	    0	    0	1
+
+    # Joint Tours
+    # hhid	type	category	composition
+    # 11111	eat	    joint	    mixed
+    # 22222	eat	    joint	    adults
+    # 22222	shop	joint	    mixed
+    # 33333	shop	joint	    adults
+
+    joint_tours = process_joint_tours_frequency_composition(
+        choices, alt_tdd, temp_point_persons
+    )
+
+    tours = pipeline.extend_table("tours", joint_tours)
+
+    tracing.register_traceable_table("tours", joint_tours)
+    pipeline.get_rn_generator().add_channel("tours", joint_tours)
+
+    # we expect there to be an alt with no tours - which we can use to backfill non-travelers
+    no_tours_alt = 0
+    households_merged["joint_tour_frequency_composition"] = (
+        choices.reindex(households_merged.index).fillna(no_tours_alt).astype(str)
+    )
+
+    households_merged["num_hh_joint_tours"] = (
+        joint_tours.groupby("household_id")
+        .size()
+        .reindex(households_merged.index)
+        .fillna(0)
+        .astype(np.int8)
+    )
+
+    pipeline.replace_table("households", households_merged)
+
+    tracing.print_summary(
+        "joint_tour_frequency_composition",
+        households_merged.joint_tour_frequency_composition,
+        value_counts=True,
+    )
+
+    if trace_hh_id:
+        tracing.trace_df(
+            households_merged, label="joint_tour_frequency_composition.households"
+        )
+
+        tracing.trace_df(
+            joint_tours,
+            label="joint_tour_frequency_composition.joint_tours",
+            slicer="household_id",
+        )
diff --git a/activitysim/abm/models/joint_tour_participation.py b/activitysim/abm/models/joint_tour_participation.py
index 939d9fedd..ee8658ae5 100644
--- a/activitysim/abm/models/joint_tour_participation.py
+++ b/activitysim/abm/models/joint_tour_participation.py
@@ -3,6 +3,7 @@
 import logging
 
 import pandas as pd
+import numpy as np
 
 from activitysim.abm.models.util.canonical_ids import MAX_PARTICIPANT_PNUM
 from activitysim.core import (
@@ -57,7 +58,7 @@ def joint_tour_participation_candidates(joint_tours, persons_merged):
 
     # - stable (predictable) index
     # if this happens, participant_id may not be unique
-    # channel random seeds will overlap at MAX_PARTICIPANT_PNUM (not probably a big deal)
+    # channel random seeds will overlap at MAX_PARTICIPANT_PNUM (probably not a big deal)
     # and estimation infer will fail
     assert (
         candidates.PNUM.max() < MAX_PARTICIPANT_PNUM
@@ -65,6 +66,7 @@ def joint_tour_participation_candidates(joint_tours, persons_merged):
     candidates["participant_id"] = (
         candidates[joint_tours.index.name] * MAX_PARTICIPANT_PNUM
     ) + candidates.PNUM
+    candidates["participant_id"] = candidates["participant_id"].astype(np.uint64)
     candidates.set_index(
         "participant_id", drop=True, inplace=True, verify_integrity=True
     )
@@ -198,7 +200,19 @@ def participants_chooser(probs, choosers, spec, trace_label):
                 transpose=False,
             )
             print(unsatisfied_candidates.head(20))
-            assert False
+
+            if model_settings.get("FORCE_PARTICIPATION", False):
+                logger.warning(
+                    f"Forcing joint tour participation for {num_tours_remaining} tours."
+                )
+                # anybody with probability > 0 is forced to join the joint tour
+                probs[choice_col] = np.where(probs[choice_col] > 0, 1, 0)
+                non_choice_col = [col for col in probs.columns if col != choice_col][0]
+                probs[non_choice_col] = 1 - probs[choice_col]
+            else:
+                raise RuntimeError(
+                    f"{num_tours_remaining} tours could not be satisfied after {iter} iterations"
+                )
 
         choices, rands = logit.make_choices(
             probs, trace_label=trace_label, trace_choosers=choosers
@@ -209,6 +223,13 @@ def participants_chooser(probs, choosers, spec, trace_label):
         tour_satisfaction = get_tour_satisfaction(candidates, participate)
         num_tours_satisfied_this_iter = tour_satisfaction.sum()
 
+        if (iter > MAX_ITERATIONS) & (
+            num_tours_remaining != num_tours_satisfied_this_iter
+        ):
+            logger.error(
+                f"Still do not satisfy participation for {num_tours_remaining - num_tours_satisfied_this_iter} tours."
+            )
+
         if num_tours_satisfied_this_iter > 0:
 
             num_tours_remaining -= num_tours_satisfied_this_iter
diff --git a/activitysim/abm/models/location_choice.py b/activitysim/abm/models/location_choice.py
index 1a7564942..dd5e279b3 100644
--- a/activitysim/abm/models/location_choice.py
+++ b/activitysim/abm/models/location_choice.py
@@ -426,6 +426,8 @@ def run_location_sample(
     )
     dest_size_terms = dest_size_terms[dest_size_terms.size_term > 0]
 
+    assert len(dest_size_terms) > 0, "No destination with available size terms"
+
     # by default, enable presampling for multizone systems, unless they disable it in settings file
     pre_sample_taz = not (network_los.zone_system == los.ONE_ZONE)
     if pre_sample_taz and not config.setting("want_dest_choice_presampling", True):
diff --git a/activitysim/abm/models/non_mandatory_tour_frequency.py b/activitysim/abm/models/non_mandatory_tour_frequency.py
index 93b36b2f4..521f49c47 100644
--- a/activitysim/abm/models/non_mandatory_tour_frequency.py
+++ b/activitysim/abm/models/non_mandatory_tour_frequency.py
@@ -271,7 +271,7 @@ def non_mandatory_tour_frequency(persons, persons_merged, chunk_size, trace_hh_i
     no_tours_alt = (alternatives.sum(axis=1) == 0).index[0]
     # need to reindex as we only handled persons with cdap_activity in ['M', 'N']
     persons["non_mandatory_tour_frequency"] = (
-        choices.reindex(persons.index).fillna(no_tours_alt).astype(np.int8)
+        choices.reindex(persons.index).fillna(no_tours_alt).astype(np.int16)
     )
 
     """
diff --git a/activitysim/abm/models/parking_location_choice.py b/activitysim/abm/models/parking_location_choice.py
index 3b10f32b4..a87703b8b 100644
--- a/activitysim/abm/models/parking_location_choice.py
+++ b/activitysim/abm/models/parking_location_choice.py
@@ -232,7 +232,6 @@ def run_parking_destination(
 
     alt_column_filter_name = model_settings.get("ALTERNATIVE_FILTER_COLUMN_NAME")
     alternatives = land_use[land_use[alt_column_filter_name]]
-
     alternatives.index.name = parking_location_column_name
 
     choices_list = []
diff --git a/activitysim/abm/models/telecommute_frequency.py b/activitysim/abm/models/telecommute_frequency.py
index cc1eec489..4596a8911 100755
--- a/activitysim/abm/models/telecommute_frequency.py
+++ b/activitysim/abm/models/telecommute_frequency.py
@@ -57,7 +57,7 @@ def telecommute_frequency(persons_merged, persons, chunk_size, trace_hh_id):
     if estimator:
         estimator.write_model_settings(model_settings, model_settings_file_name)
         estimator.write_spec(model_settings)
-        estimator.write_coefficients(coefficients_df)
+        estimator.write_coefficients(coefficients_df, model_settings)
         estimator.write_choosers(choosers)
 
     choices = simulate.simple_simulate(
diff --git a/activitysim/abm/models/transit_pass_ownership.py b/activitysim/abm/models/transit_pass_ownership.py
index 6507ab825..92d97080f 100644
--- a/activitysim/abm/models/transit_pass_ownership.py
+++ b/activitysim/abm/models/transit_pass_ownership.py
@@ -51,7 +51,7 @@ def transit_pass_ownership(persons_merged, persons, chunk_size, trace_hh_id):
     if estimator:
         estimator.write_model_settings(model_settings, model_settings_file_name)
         estimator.write_spec(model_settings)
-        estimator.write_coefficients(coefficients_df)
+        estimator.write_coefficients(coefficients_df, model_settings)
         estimator.write_choosers(choosers)
 
     choices = simulate.simple_simulate(
diff --git a/activitysim/abm/models/transit_pass_subsidy.py b/activitysim/abm/models/transit_pass_subsidy.py
index 4e513a661..45a118fda 100644
--- a/activitysim/abm/models/transit_pass_subsidy.py
+++ b/activitysim/abm/models/transit_pass_subsidy.py
@@ -51,7 +51,7 @@ def transit_pass_subsidy(persons_merged, persons, chunk_size, trace_hh_id):
     if estimator:
         estimator.write_model_settings(model_settings, model_settings_file_name)
         estimator.write_spec(model_settings)
-        estimator.write_coefficients(coefficients_df)
+        estimator.write_coefficients(coefficients_df, model_settings)
         estimator.write_choosers(choosers)
 
     choices = simulate.simple_simulate(
diff --git a/activitysim/abm/models/trip_mode_choice.py b/activitysim/abm/models/trip_mode_choice.py
index 54586a448..e7bb200e4 100644
--- a/activitysim/abm/models/trip_mode_choice.py
+++ b/activitysim/abm/models/trip_mode_choice.py
@@ -164,8 +164,10 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id):
 
     model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"])
     nest_spec = config.get_logit_model_settings(model_settings)
+    cols_to_keep = model_settings.get("CHOOSER_COLS_TO_KEEP", None)
 
     choices_list = []
+    cols_to_keep_list = []
     for primary_purpose, trips_segment in trips_merged.groupby("primary_purpose"):
 
         segment_trace_label = tracing.extend_trace_label(trace_label, primary_purpose)
@@ -250,6 +252,14 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id):
             )
 
         choices_list.append(choices)
+        if cols_to_keep:
+            cols_not_in_choosers = [
+                col for col in cols_to_keep if col not in trips_segment.columns
+            ]
+            assert (
+                len(cols_not_in_choosers) == 0
+            ), "{cols_not_in_choosers} from CHOOSER_COLS_TO_KEEP is not in the choosers dataframe"
+            cols_to_keep_list.append(trips_segment[cols_to_keep])
 
     choices_df = pd.concat(choices_list)
 
@@ -279,6 +289,12 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id):
         estimator.write_override_choices(choices_df.trip_mode)
         estimator.end_estimation()
     trips_df = trips.to_frame()
+
+    # adding columns from the chooser table to include in final output
+    if len(cols_to_keep_list) > 0:
+        cols_to_keep_df = pd.concat(cols_to_keep_list)
+        choices_df = pd.concat([choices_df, cols_to_keep_df], axis=1)
+
     assign_in_place(trips_df, choices_df)
 
     if pipeline.is_table("school_escort_tours") & model_settings.get(
@@ -301,7 +317,7 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id):
     pipeline.replace_table("trips", trips_df)
 
     if model_settings.get("annotate_trips"):
-        annotate.annotate_trips(model_settings, trace_label)
+        annotate.annotate_trips(model_settings, trace_label, locals_dict)
 
     if trace_hh_id:
         tracing.trace_df(
diff --git a/activitysim/abm/models/trip_scheduling.py b/activitysim/abm/models/trip_scheduling.py
index f8345650d..54c3eb201 100644
--- a/activitysim/abm/models/trip_scheduling.py
+++ b/activitysim/abm/models/trip_scheduling.py
@@ -1,6 +1,7 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 import logging
+import warnings
 from builtins import range
 
 import numpy as np
@@ -8,11 +9,11 @@
 
 from activitysim.abm.models.util import estimation
 from activitysim.abm.models.util.trip import cleanup_failed_trips, failed_trip_cohorts
-from activitysim.core import chunk, config, inject, logit, pipeline, tracing
+from activitysim.core import chunk, config, expressions, inject, pipeline, tracing
 from activitysim.core.util import reindex
 
-from .util.school_escort_tours_trips import split_out_school_escorting_trips
 from .util import probabilistic_scheduling as ps
+from .util.school_escort_tours_trips import split_out_school_escorting_trips
 
 logger = logging.getLogger(__name__)
 
@@ -36,6 +37,7 @@
 
 DEPARTURE_MODE = "departure"
 DURATION_MODE = "stop_duration"
+RELATIVE_MODE = "relative"
 PROBS_JOIN_COLUMNS_DEPARTURE_BASED = [
     "primary_purpose",
     "outbound",
@@ -43,6 +45,23 @@
     "trip_num",
 ]
 PROBS_JOIN_COLUMNS_DURATION_BASED = ["outbound", "stop_num"]
+PROBS_JOIN_COLUMNS_RELATIVE_BASED = ["outbound", "periods_left"]
+
+
+def _logic_version(model_settings):
+    logic_version = model_settings.get("logic_version", None)
+    if logic_version is None:
+        warnings.warn(
+            "The trip_scheduling component now has a logic_version setting "
+            "to control how the scheduling rules are applied.  The default "
+            "logic_version is currently set at `1` but may be moved up in "
+            "the future. Explicitly set `logic_version` to 2 in the model "
+            "settings to upgrade your model logic now, or set it to 1 to "
+            "suppress this message.",
+            FutureWarning,
+        )
+        logic_version = 1
+    return logic_version
 
 
 def set_tour_hour(trips, tours):
@@ -106,7 +125,7 @@ def set_stop_num(trips):
     trips["stop_num"] = trips.stop_num.where(trips["outbound"], trips["trip_num"])
 
 
-def update_tour_earliest(trips, outbound_choices):
+def update_tour_earliest(trips, outbound_choices, logic_version: int):
     """
     Updates "earliest" column for inbound trips based on
     the maximum outbound trip departure time of the tour.
@@ -119,6 +138,14 @@ def update_tour_earliest(trips, outbound_choices):
     outbound_choices: pd.Series
         time periods depart choices, one per trip (except for trips with
         zero probs)
+    logic_version : int
+        Logic version 1 is the original ActivitySim implementation, which
+        sets the "earliest" value to the max outbound departure for all
+        inbound trips, regardless of what that max outbound departure value
+        is (even if it is NA).  Logic version 2 introduces a change whereby
+        that assignment is only made if the max outbound departure value is
+        not NA.
+
     Returns
     -------
     modifies trips in place
@@ -142,9 +169,19 @@ def update_tour_earliest(trips, outbound_choices):
 
     # set the trips "earliest" column equal to the max outbound departure
     # time for all inbound trips. preserve values that were used for outbound trips
-    tmp_trips["earliest"] = tmp_trips["earliest"].where(
-        tmp_trips["outbound"], tmp_trips["max_outbound_departure"]
-    )
+    # FIXME - extra logic added because max_outbound_departure can be NA if previous failed trip was removed
+    if logic_version == 1:
+        tmp_trips["earliest"] = tmp_trips["earliest"].where(
+            tmp_trips["outbound"], tmp_trips["max_outbound_departure"]
+        )
+    elif logic_version > 1:
+        tmp_trips["earliest"] = np.where(
+            ~tmp_trips["outbound"] & ~tmp_trips["max_outbound_departure"].isna(),
+            tmp_trips["max_outbound_departure"],
+            tmp_trips["earliest"],
+        )
+    else:
+        raise ValueError(f"bad logic_version: {logic_version}")
 
     trips["earliest"] = tmp_trips["earliest"].reindex(trips.index)
 
@@ -181,6 +218,7 @@ def schedule_trips_in_leg(
     failfix = model_settings.get(FAILFIX, FAILFIX_DEFAULT)
     depart_alt_base = model_settings.get("DEPART_ALT_BASE", 0)
     scheduling_mode = model_settings.get("scheduling_mode", "departure")
+    preprocessor_settings = model_settings.get("preprocessor", None)
 
     if scheduling_mode == "departure":
         probs_join_cols = model_settings.get(
@@ -190,10 +228,14 @@ def schedule_trips_in_leg(
         probs_join_cols = model_settings.get(
             "probs_join_cols", PROBS_JOIN_COLUMNS_DURATION_BASED
         )
+    elif scheduling_mode == "relative":
+        probs_join_cols = model_settings.get(
+            "probs_join_cols", PROBS_JOIN_COLUMNS_RELATIVE_BASED
+        )
     else:
         logger.error(
             "Invalid scheduling mode specified: {0}.".format(scheduling_mode),
-            "Please select one of ['departure', 'stop_duration'] and try again.",
+            "Please select one of ['departure', 'stop_duration', 'relative'] and try again.",
         )
 
     # logger.debug("%s scheduling %s trips" % (trace_label, trips.shape[0]))
@@ -232,18 +274,34 @@ def schedule_trips_in_leg(
         ADJUST_NEXT_DEPART_COL = "latest"
     trips.next_trip_id = trips.next_trip_id.where(~is_final, NO_TRIP_ID)
 
+    network_los = inject.get_injectable("network_los")
+    locals_dict = {"network_los": network_los}
+    locals_dict.update(config.get_model_constants(model_settings))
+
     first_trip_in_leg = True
     for i in range(trips.trip_num.min(), trips.trip_num.max() + 1):
+        nth_trace_label = tracing.extend_trace_label(trace_label, "num_%s" % i)
 
-        if outbound or scheduling_mode == DURATION_MODE:
+        # - annotate trips
+        if preprocessor_settings:
+            expressions.assign_columns(
+                df=trips,
+                model_settings=preprocessor_settings,
+                locals_dict=locals_dict,
+                trace_label=nth_trace_label,
+            )
+
+        if (
+            outbound
+            or (scheduling_mode == DURATION_MODE)
+            or (scheduling_mode == RELATIVE_MODE)
+        ):
             # iterate in ascending trip_num order
             nth_trips = trips[trips.trip_num == i]
         else:
             # iterate over inbound trips in descending trip_num order, skipping the final trip
             nth_trips = trips[trips.trip_num == trips.trip_count - i]
 
-        nth_trace_label = tracing.extend_trace_label(trace_label, "num_%s" % i)
-
         choices = ps.make_scheduling_choices(
             nth_trips,
             scheduling_mode,
@@ -265,6 +323,17 @@ def schedule_trips_in_leg(
             )
             choices = choices.fillna(trips[ADJUST_NEXT_DEPART_COL])
 
+        if scheduling_mode == RELATIVE_MODE:
+            # choices are relative to the previous departure time
+            choices = nth_trips.earliest + choices
+            # need to update the departure time based on the choice
+            logic_version = _logic_version(model_settings)
+            if logic_version == 1:
+                raise ValueError(
+                    "cannot use logic version 1 with 'relative' scheduling mode"
+                )
+            update_tour_earliest(trips, choices, logic_version)
+
         # adjust allowed depart range of next trip
         has_next_trip = nth_trips.next_trip_id != NO_TRIP_ID
         if has_next_trip.any():
@@ -299,7 +368,6 @@ def run_trip_scheduling(
     trace_hh_id,
     trace_label,
 ):
-
     set_tour_hour(trips_chunk, tours)
     set_stop_num(trips_chunk)
 
@@ -328,7 +396,7 @@ def run_trip_scheduling(
 
         # departure time of last outbound trips must constrain
         # departure times for initial inbound trips
-        update_tour_earliest(trips_chunk, choices)
+        update_tour_earliest(trips_chunk, choices, _logic_version(model_settings))
 
     if (~trips_chunk.outbound).any():
         leg_chunk = trips_chunk[~trips_chunk.outbound]
@@ -353,7 +421,6 @@ def run_trip_scheduling(
 
 @inject.step()
 def trip_scheduling(trips, tours, chunk_size, trace_hh_id):
-
     """
     Trip scheduling assigns depart times for trips within the start, end limits of the tour.
 
@@ -437,9 +504,9 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id):
         ]
         estimator.write_choosers(trips_df[chooser_cols_for_estimation])
 
-    probs_spec = pd.read_csv(
-        config.config_file_path("trip_scheduling_probs.csv"), comment="#"
-    )
+    probs_spec_file = model_settings.get("PROBS_SPEC", "trip_scheduling_probs.csv")
+    logger.debug(f"probs_spec_file: {config.config_file_path(probs_spec_file)}")
+    probs_spec = pd.read_csv(config.config_file_path(probs_spec_file), comment="#")
     # FIXME for now, not really doing estimation for probabilistic model - just overwriting choices
     # besides, it isn't clear that named coefficients would be helpful if we had some form of estimation
     # coefficients_df = simulate.read_model_coefficients(model_settings)
@@ -465,13 +532,10 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id):
     ) in chunk.adaptive_chunked_choosers_by_chunk_id(
         trips_df, chunk_size, trace_label, trace_label
     ):
-
         i = 0
         while (i < max_iterations) and not trips_chunk.empty:
-
             # only chunk log first iteration since memory use declines with each iteration
             with chunk.chunk_log(trace_label) if i == 0 else chunk.chunk_log_skip():
-
                 i += 1
                 is_last_iteration = i == max_iterations
 
@@ -499,6 +563,11 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id):
                 failed = choices.reindex(trips_chunk.index).isnull()
                 logger.info("%s %s failed", trace_label_i, failed.sum())
 
+                if (failed.sum() > 0) & (
+                    model_settings.get("scheduling_mode") == "relative"
+                ):
+                    raise RuntimeError("failed trips with relative scheduling mode")
+
                 if not is_last_iteration:
                     # boolean series of trips whose leg scheduling failed
                     failed_cohorts = failed_trip_cohorts(trips_chunk, failed)
diff --git a/activitysim/abm/models/util/annotate.py b/activitysim/abm/models/util/annotate.py
index a7ef4f54c..e50519b38 100644
--- a/activitysim/abm/models/util/annotate.py
+++ b/activitysim/abm/models/util/annotate.py
@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-def annotate_tours(model_settings, trace_label):
+def annotate_tours(model_settings, trace_label, locals_dict={}):
     """
     Add columns to the tours table in the pipeline according to spec.
 
@@ -28,12 +28,13 @@ def annotate_tours(model_settings, trace_label):
     expressions.assign_columns(
         df=tours,
         model_settings=model_settings.get("annotate_tours"),
+        locals_dict=locals_dict,
         trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"),
     )
     pipeline.replace_table("tours", tours)
 
 
-def annotate_trips(model_settings, trace_label):
+def annotate_trips(model_settings, trace_label, locals_dict={}):
     """
     Add columns to the trips table in the pipeline according to spec.
 
@@ -46,6 +47,7 @@ def annotate_trips(model_settings, trace_label):
     expressions.assign_columns(
         df=trips,
         model_settings=model_settings.get("annotate_trips"),
+        locals_dict=locals_dict,
         trace_label=tracing.extend_trace_label(trace_label, "annotate_trips"),
     )
     pipeline.replace_table("trips", trips)
diff --git a/activitysim/abm/models/util/cdap.py b/activitysim/abm/models/util/cdap.py
index 7e0f55186..b71d5128a 100644
--- a/activitysim/abm/models/util/cdap.py
+++ b/activitysim/abm/models/util/cdap.py
@@ -195,6 +195,12 @@ def individual_utilities(
     useful_columns = [_hh_id_, _ptype_, "cdap_rank", _hh_size_]
     indiv_utils[useful_columns] = persons[useful_columns]
 
+    # add attributes for joint tour utility
+    model_settings = config.read_model_settings("cdap.yaml")
+    additional_useful_columns = model_settings.get("JOINT_TOUR_USEFUL_COLUMNS", None)
+    if additional_useful_columns is not None:
+        indiv_utils[additional_useful_columns] = persons[additional_useful_columns]
+
     if trace_hh_id:
         tracing.trace_df(
             indiv_utils,
@@ -259,6 +265,10 @@ def cached_spec_name(hhsize):
     return "cdap_spec_%s" % hhsize
 
 
+def cached_joint_spec_name(hhsize):
+    return "cdap_joint_spec_%s" % hhsize
+
+
 def get_cached_spec(hhsize):
 
     spec_name = cached_spec_name(hhsize)
@@ -281,14 +291,39 @@ def get_cached_spec(hhsize):
     return None
 
 
+def get_cached_joint_spec(hhsize):
+
+    spec_name = cached_joint_spec_name(hhsize)
+
+    spec = inject.get_injectable(spec_name, None)
+    if spec is not None:
+        logger.debug(
+            "build_cdap_joint_spec returning cached injectable spec %s", spec_name
+        )
+        return spec
+
+    return None
+
+
 def cache_spec(hhsize, spec):
     spec_name = cached_spec_name(hhsize)
     # cache as injectable
     inject.add_injectable(spec_name, spec)
 
 
+def cache_joint_spec(hhsize, spec):
+    spec_name = cached_joint_spec_name(hhsize)
+    # cache as injectable
+    inject.add_injectable(spec_name, spec)
+
+
 def build_cdap_spec(
-    interaction_coefficients, hhsize, trace_spec=False, trace_label=None, cache=True
+    interaction_coefficients,
+    hhsize,
+    trace_spec=False,
+    trace_label=None,
+    cache=True,
+    joint_tour_alt=False,
 ):
     """
     Build a spec file for computing utilities of alternative household member interaction patterns
@@ -355,6 +390,14 @@ def build_cdap_spec(
     # e.g. ['HH', 'HM', 'HN', 'MH', 'MM', 'MN', 'NH', 'NM', 'NN'] for hhsize=2
     alternatives = ["".join(tup) for tup in itertools.product("HMN", repeat=hhsize)]
 
+    if joint_tour_alt:
+        joint_alternatives = [
+            "".join(tup) + "J"
+            for tup in itertools.product("HMN", repeat=hhsize)
+            if tup.count("M") + tup.count("N") >= 2
+        ]
+        alternatives = alternatives + joint_alternatives
+
     # spec df has expression column plus a column for each alternative
     spec = pd.DataFrame(columns=[expression_name] + alternatives)
 
@@ -482,6 +525,207 @@ def build_cdap_spec(
     return spec
 
 
+def build_cdap_joint_spec(
+    joint_tour_coefficients, hhsize, trace_spec=False, trace_label=None, cache=True
+):
+    """
+    Build a spec file for computing joint tour utilities of alternative household member for households of specified size.
+    We generate this spec automatically from a table of rules and coefficients because the
+    interaction rules are fairly simple and can be expressed compactly whereas
+    there is a lot of redundancy between the spec files for different household sizes, as well as
+    in the vectorized expression of the interaction alternatives within the spec file itself
+    joint_tour_coefficients has five columns:
+        label
+            label of the expression
+        description
+            description of the expression
+        dependency
+            if the expression is dependent on alternative, and which alternative is it dependent on
+            (e.g. M_px, N_px, H_px)
+        expression
+            expression of the utility term
+        coefficient
+            The coefficient to apply for the alternative
+    The generated spec will have the eval expression in the index, and a utility column for each
+    alternative (e.g. ['HH', 'HM', 'HN', 'MH', 'MM', 'MN', 'NH', 'NM', 'NN', 'MMJ', 'MNJ', 'NMJ', 'NNJ'] for hhsize 2 with joint alts)
+    Parameters
+    ----------
+    joint_tour_coefficients : pandas.DataFrame
+        Rules and coefficients for generating joint tour specs for different household sizes
+    hhsize : int
+        household size for which the spec should be built.
+    Returns
+    -------
+    spec: pandas.DataFrame
+    """
+
+    t0 = tracing.print_elapsed_time()
+
+    # cdap joint spec is same for all households of MAX_HHSIZE and greater
+    hhsize = min(hhsize, MAX_HHSIZE)
+
+    if cache:
+        spec = get_cached_joint_spec(hhsize)
+        if spec is not None:
+            return spec
+
+    expression_name = "Expression"
+
+    # generate a list of activity pattern alternatives for this hhsize
+    # e.g. ['HH', 'HM', 'HN', 'MH', 'MM', 'MN', 'NH', 'NM', 'NN'] for hhsize=2
+    alternatives = ["".join(tup) for tup in itertools.product("HMN", repeat=hhsize)]
+
+    joint_alternatives = [
+        "".join(tup) + "J"
+        for tup in itertools.product("HMN", repeat=hhsize)
+        if tup.count("M") + tup.count("N") >= 2
+    ]
+    alternatives = alternatives + joint_alternatives
+
+    # spec df has expression column plus a column for each alternative
+    spec = pd.DataFrame(columns=[expression_name] + alternatives)
+
+    # Before processing the interaction_coefficients, we add add rows to the spec to carry
+    # the alternative utilities previously computed for each individual into all hh alternative
+    # columns in which the individual assigned that alternative. The Expression column contains
+    # the name of the choosers column with that individuals utility for the individual alternative
+    # and the hh alternative columns that should receive that utility are given a value of 1
+    # e.g. M_p1 is a column in choosers with the individual utility to person p1 of alternative M
+    #   Expression   MM   MN   MH   NM   NN   NH   HM   HN   HH
+    #         M_p1  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0
+    #         N_p1  0.0  0.0  0.0  1.0  1.0  1.0  0.0  0.0  0.0
+    for pnum in range(1, hhsize + 1):
+        for activity in ["M", "N", "H"]:
+
+            new_row_index = len(spec)
+            spec.loc[new_row_index, expression_name] = add_pn(activity, pnum)
+
+            # list of alternative columns where person pnum has expression activity
+            # e.g. for M_p1 we want the columns where activity M is in position p1
+            alternative_columns = [
+                alt for alt in alternatives if alt[pnum - 1] == activity
+            ]
+            spec.loc[new_row_index, alternative_columns] = 1
+
+    # for each row in the joint util table
+    for row in joint_tour_coefficients.itertuples():
+
+        # if there is no dependencies
+        if row.dependency is np.nan:
+            expression = row.Expression
+            # add a new row to spec
+            new_row_index = len(spec)
+            spec.loc[new_row_index, expression_name] = expression
+            spec.loc[new_row_index, alternatives] = row.coefficient
+        # if there is dependencies
+        else:
+            dependency_name = row.dependency
+            expression = row.Expression
+            coefficient = row.coefficient
+            if dependency_name in ["M_px", "N_px", "H_px"]:
+                if "_pxprod" in expression:
+                    prod_conds = row.Expression.split("|")
+                    expanded_expressions = [
+                        tup
+                        for tup in itertools.product(
+                            range(len(prod_conds)), repeat=hhsize
+                        )
+                    ]
+                    for expression_tup in expanded_expressions:
+                        expression_list = []
+                        dependency_list = []
+                        for counter in range(len(expression_tup)):
+                            expression_list.append(
+                                prod_conds[expression_tup[counter]].replace(
+                                    "xprod", str(counter + 1)
+                                )
+                            )
+                            if expression_tup[counter] == 0:
+                                dependency_list.append(
+                                    dependency_name.replace("x", str(counter + 1))
+                                )
+
+                        expression_value = "&".join(expression_list)
+                        dependency_value = pd.Series(
+                            np.ones(len(alternatives)), index=alternatives
+                        )
+                        if len(dependency_list) > 0:
+                            for dependency in dependency_list:
+                                # temp = spec.loc[spec[expression_name]==dependency, alternatives].squeeze().fillna(0)
+                                dependency_value *= (
+                                    spec.loc[
+                                        spec[expression_name] == dependency,
+                                        alternatives,
+                                    ]
+                                    .squeeze()
+                                    .fillna(0)
+                                )
+
+                        # add a new row to spec
+                        new_row_index = len(spec)
+                        spec.loc[new_row_index] = dependency_value
+                        spec.loc[new_row_index, expression_name] = expression_value
+                        spec.loc[new_row_index, alternatives] = (
+                            spec.loc[new_row_index, alternatives] * coefficient
+                        )
+
+                elif "_px" in expression:
+                    for pnum in range(1, hhsize + 1):
+                        dependency_name = row.dependency.replace("x", str(pnum))
+                        expression = row.Expression.replace("x", str(pnum))
+
+                        # add a new row to spec
+                        new_row_index = len(spec)
+                        spec.loc[new_row_index] = spec.loc[
+                            spec[expression_name] == dependency_name
+                        ].squeeze()
+                        spec.loc[new_row_index, expression_name] = expression
+                        spec.loc[new_row_index, alternatives] = (
+                            spec.loc[new_row_index, alternatives] * coefficient
+                        )
+
+    # drop dependency rows
+    spec = spec[~spec[expression_name].str.startswith(("M_p", "N_p", "H_p"))]
+
+    # eval expression goes in the index
+    spec.set_index(expression_name, inplace=True)
+
+    for c in spec.columns:
+        spec[c] = spec[c].fillna(0)
+
+    simulate.uniquify_spec_index(spec)
+
+    # make non-joint alts 0
+    for c in alternatives:
+        if c.endswith("J"):
+            continue
+        else:
+            spec[c] = 0
+
+    if trace_spec:
+        tracing.trace_df(
+            spec,
+            "%s.hhsize%d_joint_spec" % (trace_label, hhsize),
+            transpose=False,
+            slicer="NONE",
+        )
+
+    if trace_spec:
+        tracing.trace_df(
+            spec,
+            "%s.hhsize%d_joint_spec_patched" % (trace_label, hhsize),
+            transpose=False,
+            slicer="NONE",
+        )
+
+    if cache:
+        cache_joint_spec(hhsize, spec)
+
+    t0 = tracing.print_elapsed_time("build_cdap_joint_spec hh_size %s" % hhsize, t0)
+
+    return spec
+
+
 def add_interaction_column(choosers, p_tup):
     """
     Add an interaction column in place to choosers, listing the ptypes of the persons in p_tup
@@ -578,6 +822,12 @@ def hh_choosers(indiv_utils, hhsize):
     # we want to merge the ptype and M, N, and H utilities for each individual in the household
     merge_cols = [_hh_id_, _ptype_, "M", "N", "H"]
 
+    # add attributes for joint tour utility
+    model_settings = config.read_model_settings("cdap.yaml")
+    additional_merge_cols = model_settings.get("JOINT_TOUR_USEFUL_COLUMNS", None)
+    if additional_merge_cols is not None:
+        merge_cols.extend(additional_merge_cols)
+
     if hhsize > MAX_HHSIZE:
         raise RuntimeError("hh_choosers hhsize > MAX_HHSIZE")
 
@@ -620,11 +870,19 @@ def hh_choosers(indiv_utils, hhsize):
         for tup in itertools.combinations(list(range(1, hhsize + 1)), i):
             add_interaction_column(choosers, tup)
 
+    # add hhsize
+    choosers["hhsize"] = hhsize
+
     return choosers
 
 
 def household_activity_choices(
-    indiv_utils, interaction_coefficients, hhsize, trace_hh_id=None, trace_label=None
+    indiv_utils,
+    interaction_coefficients,
+    hhsize,
+    trace_hh_id=None,
+    trace_label=None,
+    add_joint_tour_utility=False,
 ):
     """
     Calculate household utilities for each activity pattern alternative for households of hhsize
@@ -669,6 +927,7 @@ def household_activity_choices(
             hhsize,
             trace_spec=(trace_hh_id in choosers.index),
             trace_label=trace_label,
+            joint_tour_alt=add_joint_tour_utility,
         )
 
         utils = simulate.eval_utilities(spec, choosers, trace_label=trace_label)
@@ -676,6 +935,23 @@ def household_activity_choices(
     if len(utils.index) == 0:
         return pd.Series(dtype="float64")
 
+    # calculate joint tour utility
+    if add_joint_tour_utility & (hhsize > 1):
+        # calculate joint utils
+        joint_tour_spec = build_cdap_joint_spec(
+            interaction_coefficients,
+            hhsize,
+            trace_spec=(trace_hh_id in choosers.index),
+            trace_label=trace_label,
+        )
+
+        joint_tour_utils = simulate.eval_utilities(
+            joint_tour_spec, choosers, trace_label=trace_label
+        )
+
+        # add joint util to util
+        utils = utils.add(joint_tour_utils)
+
     probs = logit.utils_to_probs(utils, trace_label=trace_label)
 
     # select an activity pattern alternative for each household based on probability
@@ -867,6 +1143,7 @@ def _run_cdap(
     locals_d,
     trace_hh_id,
     trace_label,
+    add_joint_tour_utility,
 ):
     """
     Implements core run_cdap functionality on persons df (or chunked subset thereof)
@@ -909,6 +1186,7 @@ def _run_cdap(
             hhsize=hhsize,
             trace_hh_id=trace_hh_id,
             trace_label=trace_label,
+            add_joint_tour_utility=add_joint_tour_utility,
         )
 
         hh_choices_list.append(choices)
@@ -940,6 +1218,13 @@ def _run_cdap(
     persons["cdap_activity"] = person_choices
     chunk.log_df(trace_label, "persons", persons)
 
+    # return household joint tour flag
+    if add_joint_tour_utility:
+        hh_activity_choices = hh_activity_choices.to_frame(name="hh_choices")
+        hh_activity_choices["has_joint_tour"] = hh_activity_choices["hh_choices"].apply(
+            lambda x: 1 if "J" in x else 0
+        )
+
     # if DUMP:
     #     tracing.trace_df(hh_activity_choices, '%s.DUMP.hh_activity_choices' % trace_label,
     #                      transpose=False, slicer='NONE')
@@ -951,7 +1236,10 @@ def _run_cdap(
     del persons
     chunk.log_df(trace_label, "persons", None)
 
-    return result
+    if add_joint_tour_utility:
+        return result, hh_activity_choices["has_joint_tour"]
+    else:
+        return result
 
 
 def run_cdap(
@@ -964,6 +1252,7 @@ def run_cdap(
     chunk_size=0,
     trace_hh_id=None,
     trace_label=None,
+    add_joint_tour_utility=False,
 ):
     """
     Choose individual activity patterns for persons.
@@ -990,6 +1279,8 @@ def run_cdap(
         hh_id to trace or None if no hh tracing
     trace_label : str
         label for tracing or None if no tracing
+    add_joint_tour_utility : Bool
+        cdap model include joint tour utility or not
 
     Returns
     -------
@@ -1011,16 +1302,30 @@ def run_cdap(
         chunk_trace_label,
     ) in chunk.adaptive_chunked_choosers_by_chunk_id(persons, chunk_size, trace_label):
 
-        cdap_results = _run_cdap(
-            persons_chunk,
-            person_type_map,
-            cdap_indiv_spec,
-            cdap_interaction_coefficients,
-            cdap_fixed_relative_proportions,
-            locals_d,
-            trace_hh_id,
-            chunk_trace_label,
-        )
+        if add_joint_tour_utility:
+            cdap_results, hh_choice_results = _run_cdap(
+                persons_chunk,
+                person_type_map,
+                cdap_indiv_spec,
+                cdap_interaction_coefficients,
+                cdap_fixed_relative_proportions,
+                locals_d,
+                trace_hh_id,
+                chunk_trace_label,
+                add_joint_tour_utility,
+            )
+        else:
+            cdap_results = _run_cdap(
+                persons_chunk,
+                person_type_map,
+                cdap_indiv_spec,
+                cdap_interaction_coefficients,
+                cdap_fixed_relative_proportions,
+                locals_d,
+                trace_hh_id,
+                chunk_trace_label,
+                add_joint_tour_utility,
+            )
 
         result_list.append(cdap_results)
 
@@ -1042,4 +1347,8 @@ def run_cdap(
         )
 
     # return choices column as series
-    return cdap_results["cdap_activity"]
+    if add_joint_tour_utility:
+        return cdap_results["cdap_activity"], hh_choice_results
+    else:
+        # return choices column as series
+        return cdap_results["cdap_activity"]
diff --git a/activitysim/abm/models/util/probabilistic_scheduling.py b/activitysim/abm/models/util/probabilistic_scheduling.py
index aab2e8d70..6fc7689a6 100644
--- a/activitysim/abm/models/util/probabilistic_scheduling.py
+++ b/activitysim/abm/models/util/probabilistic_scheduling.py
@@ -172,10 +172,22 @@ def _preprocess_scheduling_probs(
         )
     elif scheduling_mode == "stop_duration":
         chooser_probs = _preprocess_stop_duration_probs(choosers)
+    elif scheduling_mode == "relative":
+        # creating a dataframe with just the trip_id as index and alternatives as columns
+        probs_cols = [
+            c
+            for c in probs_spec.columns
+            if ((c not in probs_join_cols) & (c.isnumeric()))
+        ]
+        chooser_probs = choosers.loc[:, probs_cols]
+        chooser_probs = chooser_probs.div(chooser_probs.sum(axis=1), axis=0)
+        assert (
+            ~chooser_probs.isna().values.any()
+        ), f"Missing probabilities for trips \n {chooser_probs[chooser_probs.isna().any(axis=1)].index}"
     else:
         logger.error(
             "Invalid scheduling mode specified: {0}.".format(scheduling_mode),
-            "Please select one of ['departure', 'stop_duration'] and try again.",
+            "Please select one of ['departure', 'stop_duration', 'relative'] and try again.",
         )
 
     # probs should sum to 1 with residual probs resulting in choice of 'fail'
@@ -196,6 +208,12 @@ def _postprocess_scheduling_choices(
     # convert alt choice index to depart time (setting failed choices to -1)
     failed = choices == choice_cols.get_loc("fail")
 
+    if scheduling_mode == "relative":
+        if failed.any():
+            RuntimeError(
+                f"Failed trips in realtive mode for {failed.sum()} trips: {choosers[failed]}"
+            )
+
     # For the stop duration-based probabilities, the alternatives are offsets that
     # get applied to trip-specific departure and arrival times, so depart_alt_base
     # is a column/series rather than a scalar.
@@ -334,7 +352,11 @@ def make_scheduling_choices(
     if failed.any():
         choices = choices[~failed]
 
-    if all([check_col in choosers_df.columns for check_col in ["earliest", "latest"]]):
+    if all(
+        [check_col in choosers_df.columns for check_col in ["earliest", "latest"]]
+    ) & (scheduling_mode != "relative"):
+        # check to make sure choice does not come before previously scheduled trip or after end of tour
+        # does not apply if choices are relative to previous trip depart
         assert (choices >= choosers_df.earliest[~failed]).all()
         assert (choices <= choosers_df.latest[~failed]).all()
 
diff --git a/activitysim/abm/models/util/tour_frequency.py b/activitysim/abm/models/util/tour_frequency.py
index 37108b564..a73482fe5 100644
--- a/activitysim/abm/models/util/tour_frequency.py
+++ b/activitysim/abm/models/util/tour_frequency.py
@@ -5,6 +5,7 @@
 import numpy as np
 import pandas as pd
 
+from activitysim.core import config
 from activitysim.abm.models.util.canonical_ids import set_tour_index
 from activitysim.core.util import reindex
 
@@ -459,3 +460,283 @@ def process_joint_tours(joint_tour_frequency, joint_tour_frequency_alts, point_p
     17978574               joint                 4    5143198
     """
     return tours
+
+
+def process_joint_tours_frequency_composition(
+    joint_tour_frequency_composition,
+    joint_tour_frequency_composition_alts,
+    point_persons,
+):
+    """
+    This method processes the joint_tour_frequency_composition column that comes out of
+    the model of the same name and turns into a DataFrame that represents the
+    joint tours that were generated
+
+    Parameters
+    ----------
+    joint_tour_frequency_composition : pandas.Series
+        household joint_tour_frequency_composition (which came out of the joint tour frequency composition model)
+        indexed by household_id
+    joint_tour_frequency_composition_alts: DataFrame
+        A DataFrame which has as a unique index with joint_tour_frequency_composition values
+        and frequency counts for the tours to be generated for that choice
+    point_persons : pandas DataFrame
+        table with columns for (at least) person_ids and home_zone_id indexed by household_id
+
+    Returns
+    -------
+    tours : DataFrame
+        An example of a tours DataFrame is supplied as a comment in the
+        source code - it has an index which is a tour identifier, a household_id
+        column, a tour_type column, composition column and tour_type_num and tour_num columns
+        which is set to 1 or 2 depending whether it is the first or second joint tour
+        made by the household.
+    """
+
+    assert not joint_tour_frequency_composition.isnull().any()
+
+    tours = process_tours_frequency_composition(
+        joint_tour_frequency_composition.dropna(),
+        joint_tour_frequency_composition_alts,
+        tour_category="joint",
+        parent_col="household_id",
+    )
+
+    assert not tours.index.duplicated().any()
+    assert point_persons.index.name == "household_id"
+
+    # - assign a temp point person to tour so we can create stable index
+    tours["person_id"] = reindex(point_persons.person_id, tours.household_id)
+    tours["origin"] = reindex(point_persons.home_zone_id, tours.household_id)
+
+    # assign stable (predictable) tour_id
+    set_tour_index(tours, is_joint=True)
+
+    """
+                   household_id tour_type  tour_type_count  tour_type_num  tour_num  tour_count
+    tour_id
+    3209530              320953      disc                1              1         1           2
+    3209531              320953      disc                2              2         2           2
+    23267026            2326702      shop                1              1         1           1
+    17978574            1797857      main                1              1         1           1
+
+                   tour_category  tour_category_id  person_id
+    3209530                joint                 4     577234
+    3209531                joint                 4     577234
+    23267026               joint                 4    1742708
+    17978574               joint                 4    5143198
+    """
+    return tours
+
+
+def process_tours_frequency_composition(
+    joint_tour_frequency_composition,
+    joint_tour_frequency_composition_alts,
+    tour_category,
+    parent_col="person_id",
+):
+    """
+    This method processes the joint_tour_frequency_composition column that comes
+    out of the model of the same name and turns into a DataFrame that
+    represents the tours that were generated
+
+    Parameters
+    ----------
+    joint_tour_frequency_composition: Series
+        A series which has <parent_col> as the index and the chosen alternative
+        index as the value
+    joint_tour_frequency_composition_alts: DataFrame
+        A DataFrame which has as a unique index which relates to the values
+        in the series above typically includes columns which are named for trip
+        purposes with values which are counts for that trip purpose, and tour composition.  Example
+        trip purposes include escort, shopping, othmaint, othdiscr, eatout,
+        social, etc. Tour composition includes adults, children, and mixed.
+        A row would be an alternative which might be to take
+        one shopping trip in a adult-only tour and zero trips of other purposes, etc.
+    tour_category : str
+        one of 'mandatory', 'non_mandatory', 'atwork', or 'joint'
+    parent_col: str
+        the name of the index (parent_tour_id for atwork subtours, otherwise person_id)
+
+    Returns
+    -------
+    tours : pandas.DataFrame
+        An example of a tours DataFrame is supplied as a comment in the
+        source code - it has an index which is a unique tour identifier,
+        a person_id column, and a tour type column which comes from the
+        column names of the alternatives DataFrame supplied above.
+
+        tours.tour_type       - tour type (e.g. school, work, shopping, eat)
+        tours.composition     - tour composition (e.g. adults, children, mixed)
+        tours.tour_type_num   - if there are two 'school' type tours, they will be numbered 1 and 2
+        tours.tour_type_count - number of tours of tour_type parent has (parent's max tour_type_num)
+        tours.tour_num        - index of tour (of any type) for parent
+        tours.tour_count      - number of tours of any type) for parent (parent's max tour_num)
+        tours.tour_category   - one of 'mandatory', 'non_mandatory', 'atwork', or 'joint'
+    """
+
+    # get the actual alternatives for each person - have to go back to the
+    # non_mandatory_tour_frequency_alts dataframe to get this - the choice
+    # above just stored the index values for the chosen alts
+    tour_counts = joint_tour_frequency_composition_alts.loc[
+        joint_tour_frequency_composition
+    ]
+
+    # assign person ids to the index
+    tour_counts.index = joint_tour_frequency_composition.index
+
+    """
+               alt1       alt2     alt3
+    <parent_col>
+    2588676       2         0         0
+    2588677       1         1         0
+    """
+
+    tours = create_joint_tours(tour_counts, tour_category, parent_col)
+
+    return tours
+
+
+def create_joint_tours(tour_counts, tour_category, parent_col="person_id"):
+    """
+    This method processes the tour_frequency column that comes
+    out of the model of the same name and turns into a DataFrame that
+    represents the tours that were generated
+
+    Parameters
+    ----------
+    tour_counts: DataFrame
+        table specifying how many tours of each type to create
+        one row per person (or parent_tour for atwork subtours)
+        one (int) column per tour_type, with number of tours to create
+    tour_category : str
+        one of 'mandatory', 'non_mandatory', 'atwork', or 'joint'
+
+    Returns
+    -------
+    tours : pandas.DataFrame
+        An example of a tours DataFrame is supplied as a comment in the
+        source code - it has an index which is a unique tour identifier,
+        a person_id column, and a tour type column which comes from the
+        column names of the alternatives DataFrame supplied above.
+
+        tours.tour_type       - tour type (e.g. school, work, shopping, eat)
+        tours.composition     - tour composition (e.g. adults, children, mixed)
+        tours.tour_type_num   - if there are two 'school' type tours, they will be numbered 1 and 2
+        tours.tour_type_count - number of tours of tour_type parent has (parent's max tour_type_num)
+        tours.tour_num        - index of tour (of any type) for parent
+        tours.tour_count      - number of tours of any type) for parent (parent's max tour_num)
+        tours.tour_category   - one of 'mandatory', 'non_mandatory', 'atwork', or 'joint'
+    """
+
+    # FIXME - document requirement to ensure adjacent tour_type_nums in tour_num order
+
+    """
+               alt1       alt2     alt3
+    <parent_col>
+    2588676       2         0         0
+    2588677       1         1         0
+    """
+    model_settings_file_name = "joint_tour_frequency_composition.yaml"
+
+    model_settings = config.read_model_settings(model_settings_file_name)
+
+    alts_table_structure = model_settings.get("ALTS_TABLE_STRUCTURE", None)
+    assert (
+        alts_table_structure is not None
+    ), f"Expected to find ALTS_TABLE_STRUCTURE setting in joint_tour_frequency_composition.yaml"
+
+    tour_type_dict = alts_table_structure.get("PURPOSE", None).get("VALUE_MAP", None)
+    assert (
+        tour_type_dict is not None
+    ), f"Expected to find PURPOSE.VALUE_MAP setting in ALTS_TABLE_STRUCTURE"
+
+    tour_type_cols = alts_table_structure.get("PURPOSE", None).get("COLUMNS", None)
+    assert (
+        tour_type_cols is not None
+    ), f"Expected to find PURPOSE.COLUMNS setting in ALTS_TABLE_STRUCTURE"
+
+    tour_comp_dict = alts_table_structure.get("COMPOSITION", None).get(
+        "VALUE_MAP", None
+    )
+    assert (
+        tour_comp_dict is not None
+    ), f"Expected to find COMPOSITION.VALUE_MAP setting in ALTS_TABLE_STRUCTURE"
+
+    tour_comp_cols = alts_table_structure.get("COMPOSITION", None).get("COLUMNS", None)
+    assert (
+        tour_comp_cols is not None
+    ), f"Expected to find COMPOSITION.COLUMNS setting in ALTS_TABLE_STRUCTURE"
+
+    # reformat with the columns given below
+    tours_purp = tour_counts[tour_type_cols].stack().reset_index()
+    tours_purp.columns = [parent_col, "tour_id_temp", "tour_type"]
+    tours_purp["tour_id_temp"] = range(1, 1 + len(tours_purp))
+    tours_purp["tour_type"] = tours_purp["tour_type"].map(tour_type_dict)
+
+    """
+        <parent_col> tour_id_temp  tour_type
+    0     2588676    purpose1           5
+    1     2588676    purpose2           0
+    2     2588677    purpose1           5
+    3     2588677    purpose2           5
+    4     2588678    purpose1           6
+    5     2588678    purpose2           7
+
+    parent_col is the index from non_mandatory_tour_frequency
+    tour_type is the column name from non_mandatory_tour_frequency_alts
+    tour_type_count is the count value of the tour's chosen alt's tour_type from alts table
+    """
+    tours_comp = tour_counts[tour_comp_cols].stack().reset_index()
+    tours_comp.columns = [parent_col, "tour_id_temp", "composition"]
+    tours_comp["tour_id_temp"] = range(1, 1 + len(tours_comp))
+    tours_comp["composition"] = tours_comp["composition"].map(tour_comp_dict)
+
+    """
+        <parent_col> tour_id_temp  tour_composition
+    0     2588676    party1           1
+    1     2588676    party2           0
+    2     2588677    party1           1
+    3     2588677    party2           1
+    4     2588678    party1           1
+    5     2588678    party2           2
+
+    parent_col is the index from non_mandatory_tour_frequency
+    tour_type is the column name from non_mandatory_tour_frequency_alts
+    tour_type_count is the count value of the tour's chosen alt's tour_type from alts table
+    """
+    tours = pd.merge(
+        tours_purp, tours_comp, how="left", on=[parent_col, "tour_id_temp"]
+    )
+
+    tours = tours[(tours.tour_type.notnull()) & (tours.tour_type.notnull())]
+
+    grouped = tours.groupby([parent_col, "tour_type"])
+    tours["tour_type_num"] = grouped.cumcount() + 1
+    tours["tour_type_count"] = tours["tour_type_num"] + grouped.cumcount(
+        ascending=False
+    )
+
+    grouped = tours.groupby(parent_col)
+    tours["tour_num"] = grouped.cumcount() + 1
+    tours["tour_count"] = tours["tour_num"] + grouped.cumcount(ascending=False)
+
+    """
+        <parent_col> tour_type  tour_type_num  tour_type_count tour_num  tour_count
+    0     2588676       alt1           1           2               1         4
+    0     2588676       alt1           2           2               2         4
+    0     2588676       alt2           1           1               3         4
+    0     2588676       alt3           1           1               4         4
+    """
+
+    # set these here to ensure consistency across different tour categories
+    assert tour_category in ["mandatory", "non_mandatory", "atwork", "joint"]
+    tours["tour_category"] = tour_category
+
+    # for joint tours, the correct number will be filled in after participation step
+    tours["number_of_participants"] = 1
+
+    # index is arbitrary but don't want any duplicates in index
+    tours.reset_index(drop=True, inplace=True)
+
+    return tours
diff --git a/activitysim/abm/models/vehicle_type_choice.py b/activitysim/abm/models/vehicle_type_choice.py
index 95723eb9d..324e505dd 100644
--- a/activitysim/abm/models/vehicle_type_choice.py
+++ b/activitysim/abm/models/vehicle_type_choice.py
@@ -124,7 +124,7 @@ def annotate_vehicle_type_choice_persons(model_settings, trace_label):
         model_settings=model_settings.get("annotate_persons"),
         trace_label=tracing.extend_trace_label(trace_label, "annotate_persons"),
     )
-    pipeline.replace_table("persons", households)
+    pipeline.replace_table("persons", persons)
 
 
 def annotate_vehicle_type_choice_vehicles(model_settings, trace_label):
@@ -214,9 +214,14 @@ def construct_model_alternatives(model_settings, alts_cats_dict, vehicle_type_da
             alts_wide._merge == "left_only", ["body_type", "fuel_type", "age"]
         ]
 
-        assert (
-            len(missing_alts) == 0
-        ), f"missing vehicle data for alternatives:\n {missing_alts}"
+        if model_settings.get("REQUIRE_DATA_FOR_ALL_ALTS", False):
+            # fail if alternative does not have an associated record in the data
+            assert (
+                len(missing_alts) == 0
+            ), f"missing vehicle data for alternatives:\n {missing_alts}"
+        else:
+            # eliminate alternatives if no vehicle type data
+            alts_wide = alts_wide[alts_wide._merge != "left_only"]
         alts_wide.drop(columns="_merge", inplace=True)
 
     # converting age to integer to allow interactions in utilities
diff --git a/activitysim/abm/models/work_from_home.py b/activitysim/abm/models/work_from_home.py
index 97a80d301..043264000 100755
--- a/activitysim/abm/models/work_from_home.py
+++ b/activitysim/abm/models/work_from_home.py
@@ -58,7 +58,7 @@ def work_from_home(persons_merged, persons, chunk_size, trace_hh_id):
     if estimator:
         estimator.write_model_settings(model_settings, model_settings_file_name)
         estimator.write_spec(model_settings)
-        estimator.write_coefficients(coefficients_df)
+        estimator.write_coefficients(coefficients_df, model_settings)
         estimator.write_choosers(choosers)
 
     # - iterative single process what-if adjustment if specified
diff --git a/activitysim/abm/tables/shadow_pricing.py b/activitysim/abm/tables/shadow_pricing.py
index 3a3ba7f56..396cf7c56 100644
--- a/activitysim/abm/tables/shadow_pricing.py
+++ b/activitysim/abm/tables/shadow_pricing.py
@@ -61,6 +61,12 @@
     "workplace": "income_segment",
 }
 
+default_segment_to_name_dict = {
+    # model_selector : persons_segment_name
+    "school": "school_segment",
+    "workplace": "income_segment",
+}
+
 
 def size_table_name(model_selector):
     """
diff --git a/activitysim/cli/create.py b/activitysim/cli/create.py
index ec545d378..7cd8d218c 100644
--- a/activitysim/cli/create.py
+++ b/activitysim/cli/create.py
@@ -123,7 +123,7 @@ def get_example(
         Files downloaded via http pointers will be cached in
         this location.  If a path is not given but just a truthy
         value, then a cache directory is created using in a location
-        selected by the appdirs library (or, if not installed,
+        selected by the platformdirs library (or, if not installed,
         linking is skipped.)
     """
     if example_name not in EXAMPLES:
@@ -200,11 +200,11 @@ def download_asset(url, target_path, sha256=None, link=True):
     if link:
         if not isinstance(link, (str, Path)):
             try:
-                import appdirs
+                import platformdirs
             except ImportError:
                 link = False
             else:
-                link = appdirs.user_data_dir("ActivitySim")
+                link = platformdirs.user_data_dir("ActivitySim")
         original_target_path = target_path
         target_path = os.path.join(link, target_path)
     os.makedirs(os.path.dirname(target_path), exist_ok=True)
diff --git a/activitysim/core/config.py b/activitysim/core/config.py
index 7024d0512..aaa7ed2fb 100644
--- a/activitysim/core/config.py
+++ b/activitysim/core/config.py
@@ -750,6 +750,14 @@ def filter_warnings():
     else:
         warnings.filterwarnings("default", category=CacheMissWarning)
 
+    # beginning from PR #660 (after 1.2.0), a FutureWarning is emitted when the trip
+    # scheduling component lacks a logic_version setting
+    warnings.filterwarnings(
+        "ignore",
+        category=FutureWarning,
+        message="The trip_scheduling component now has a logic_version setting.*",
+    )
+
 
 def handle_standard_args(parser=None):
 
diff --git a/activitysim/core/configuration.py b/activitysim/core/configuration.py
new file mode 100644
index 000000000..3174653b2
--- /dev/null
+++ b/activitysim/core/configuration.py
@@ -0,0 +1,291 @@
+from typing import Union
+
+try:
+    from pydantic import BaseModel as PydanticBase
+except ModuleNotFoundError:
+
+    class PydanticBase:
+        pass
+
+
+class InputTable(PydanticBase):
+    """
+    The features that define an input table to be read by ActivitySim.
+    """
+
+    tablename: str
+    """Name of the injected table"""
+
+    filename: str = None
+    """
+    Name of the CSV or HDF5 file to read.
+
+    If not provided, defaults to `input_store`
+    """
+
+    index_col: str = None
+    """table column to use for the index"""
+
+    rename_columns: dict[str, str] = None
+    """dictionary of column name mappings"""
+
+    keep_columns: list[str] = None
+    """
+    columns to keep once read in to memory.
+
+    Save only the columns needed for modeling or analysis to save on memory
+    and file I/O
+    """
+
+    h5_tablename: str = None
+    """table name if reading from HDF5 and different from `tablename`"""
+
+
+class Settings(PydanticBase):
+    """
+    The overall settings for the ActivitySim model system.
+
+    The input for these settings is typically stored in one main YAML file,
+    usually called ``settings.yaml``.
+
+    Note that this implementation is presently used only for generating
+    documentation, but future work may migrate the settings implementation to
+    actually use this pydantic code to validate the settings before running
+    the model.
+    """
+
+    models: list[str]
+    """
+    list of model steps to run - auto ownership, tour frequency, etc.
+
+    See :ref:`model_steps` for more details about each step.
+    """
+
+    resume_after: str = None
+    """to resume running the data pipeline after the last successful checkpoint"""
+
+    input_table_list: list[InputTable]
+    """list of table names, indices, and column re-maps for each table in `input_store`"""
+
+    input_store: str = None
+    """HDF5 inputs file"""
+
+    create_input_store: bool = False
+    """
+    Write the inputs as read in back to an HDF5 store.
+
+    If enabled, this writes the store to the outputs folder to use for subsequent
+    model runs, as reading HDF5 can be faster than reading CSV files."""
+
+    households_sample_size: int = None
+    """
+    Number of households to sample and simulate
+
+    If omitted or set to 0, ActivitySim will simulate all households.
+    """
+    trace_hh_id: Union[int, list] = None
+    """
+    Trace household id(s)
+
+    If omitted, no tracing is written out
+    """
+
+    trace_od: list[int] = None
+    """
+    Trace origin, destination pair in accessibility calculation
+
+    If omitted, no tracing is written out.
+    """
+
+    chunk_training_mode: str = None
+    """
+    The method to use for chunk training.
+
+    Valid values include {disabled, training, production, adaptive}.
+    See :ref:`chunk_size` for more details.
+    """
+
+    chunk_size: int = None
+    """
+    Approximate amount of RAM to allocate to ActivitySim for batch processing.
+
+    See :ref:`chunk_size` for more details.
+    """
+
+    chunk_method: str = None
+    """
+    Memory use measure to use for chunking.
+
+    See :ref:`chunk_size`.
+    """
+
+    checkpoints: Union[bool, list] = True
+    """
+    When to write checkpoint (intermediate table states) to disk.
+
+    If True, checkpoints are written at each step. If False, no intermediate
+    checkpoints will be written before the end of run.  Or, provide an explicit
+    list of models to checkpoint.
+    """
+
+    check_for_variability: bool = False
+    """
+    Debugging feature to find broken model specifications.
+
+    Enabling this check does not alter valid results but slows down model runs.
+    """
+
+    log_alt_losers: bool = False
+    """
+    Write out expressions when all alternatives are unavailable.
+
+    This can be useful for model development to catch errors in specifications.
+    Enabling this check does not alter valid results but slows down model runs.
+    """
+
+    use_shadow_pricing: bool = False
+    """turn shadow_pricing on and off for work and school location"""
+
+    output_tables: list[str] = None
+    """list of output tables to write to CSV or HDF5"""
+
+    want_dest_choice_sample_tables: bool = False
+    """turn writing of sample_tables on and off for all models"""
+
+    cleanup_pipeline_after_run: bool = False
+    """
+    Cleans up pipeline after successful run.
+
+    This will clean up pipeline only after successful runs, by creating a
+    single-checkpoint pipeline file, and deleting any subprocess pipelines.
+    """
+
+    sharrow: Union[bool, str] = False
+    """
+    Set the sharrow operating mode.
+
+    .. versionadded:: 1.2
+
+    * `false` - Do not use sharrow.  This is the default if no value is given.
+    * `true` - Use sharrow optimizations when possible, but fall back to
+      legacy `pandas.eval` systems when any error is encountered.  This is the
+      preferred mode for running with sharrow if reliability is more important
+      than performance.
+    * `require` - Use sharrow optimizations, and raise an error if they fail
+      unexpectedly.  This is the preferred mode for running with sharrow
+      if performance is a concern.
+    * `test` - Run every relevant calculation using both sharrow and legacy
+      systems, and compare them to ensure the results match.  This is the slowest
+      mode of operation, but useful for development and debugging.
+    """
+
+
+class ZarrDigitalEncoding(PydanticBase):
+    """Digital encoding instructions for skim tables.
+
+    .. versionadded:: 1.2
+    """
+
+    regex: str
+    """A regular expression for matching skim matrix names.
+
+    All skims with names that match under typical regular expression rules
+    for Python will be processed together.
+    """
+
+    joint_dict: str
+    """The name of the joint dictionary for this group.
+
+    This must be a unique name for this set of skims, and a new array
+    will be added to the Dataset with this name.  It will be an integer-
+    type array indicating the position of each element in the jointly
+    encoded dictionary."""
+
+
+class TAZ_Settings(PydanticBase):
+    """
+    Complex settings for TAZ skims that are not just OMX file(s).
+
+    .. versionadded:: 1.2
+    """
+
+    omx: str = None
+    """The filename of the data stored in OMX format.
+
+    This is treated as a fallback for the raw input data, if ZARR format data
+    is not available.
+    """
+
+    zarr: str = None
+    """The filename of the data stored in ZARR format.
+
+    Reading ZARR data can be much faster than reading OMX format data, so if
+    this filename is given, the ZARR file format is preferred if it exists. If
+    it does not exist, then OMX data is read in and then ZARR data is written
+    out for future usage.
+
+    .. versionadded:: 1.2
+    """
+
+    zarr_digital_encoding: list[ZarrDigitalEncoding] = None
+    """
+    A list of encodings to apply before saving skims in ZARR format.
+
+    .. versionadded:: 1.2
+    """
+
+
+class NetworkSettings(PydanticBase):
+    """
+    Network level of service and skims settings
+
+    The input for these settings is typically stored in one YAML file,
+    usually called ``network_los.yaml``.
+    """
+
+    zone_system: int
+    """Which zone system type is used.
+
+    * 1 - TAZ only.
+    * 2 - MAZ and TAZ.
+    * 3 - MAZ, TAZ, and TAP
+    """
+
+    taz_skims: Union[str, TAZ_Settings] = None
+    """Instructions for how to load and pre-process skim matrices.
+
+    If given as a string, it is interpreted as the location for OMX file(s),
+    either as a single file or as a glob-matching pattern for multiple files.
+    The time period for the matrix must be represented at the end of the matrix
+    name and be seperated by a double_underscore (e.g. `BUS_IVT__AM` indicates base
+    skim BUS_IVT with a time period of AM.
+
+    Alternatively, this can be given as a nested dictionary defined via the
+    TAZ_Settings class, which allows for ZARR transformation and pre-processing.
+    """
+
+    skim_time_periods: dict
+    """time period upper bound values and labels
+
+    * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+    * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole factor of ``time_window``. (Default: 60 minutes)
+    * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+    * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+    """
+
+    read_skim_cache: bool = False
+    """Read cached skims (using numpy memmap) from output directory.
+
+    Reading from memmap is much faster than omx, but the memmap is a huge
+    uncompressed file.
+    """
+
+    write_skim_cache: bool = False
+    """Write memmapped cached skims to output directory.
+
+    This is needed if you want to use the cached skims to speed up subsequent
+    runs.
+    """
+
+    cache_dir: str = None
+    """alternate dir to read/write cache files (defaults to output_dir)"""
diff --git a/activitysim/core/los.py b/activitysim/core/los.py
index e69ed587f..c1c3a8451 100644
--- a/activitysim/core/los.py
+++ b/activitysim/core/los.py
@@ -2,6 +2,7 @@
 # See full license in LICENSE.txt.
 
 import logging
+import os
 import warnings
 
 import numpy as np
diff --git a/activitysim/core/pathbuilder_cache.py b/activitysim/core/pathbuilder_cache.py
index 1bd219693..5153cf160 100644
--- a/activitysim/core/pathbuilder_cache.py
+++ b/activitysim/core/pathbuilder_cache.py
@@ -9,6 +9,7 @@
 
 import numpy as np
 import pandas as pd
+import psutil
 
 from activitysim.core import config, inject, simulate, util
 
diff --git a/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore b/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore
index 1d085cacc..3dd2e62f9 100644
--- a/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore
+++ b/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore
@@ -1 +1,2 @@
-**
+*.mmap
+*.feather
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv
old mode 100644
new mode 100755
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_scheduling_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_preprocessor.csv
new file mode 100644
index 000000000..732a58d47
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_preprocessor.csv
@@ -0,0 +1,8 @@
+Description,Target,Expression
+,periods_left,(df.latest - df.earliest)
+# binning the periods differently for inbound and outbound
+,periods_left_min,"np.where(df['outbound'], periods_left.clip(upper=25), periods_left.clip(upper=34))"
+,periods_left_max,"np.where(((periods_left >= 25) & (df['outbound'])) | ((periods_left >= 34) & (~df['outbound'])), 47, periods_left)"
+,tour_purpose,"reindex(tours.tour_type, df.tour_id)"
+,tour_purpose_grouped,"np.where(tour_purpose.isin(['work','school','univ']), 'mand', 'non_mand')"
+,half_tour_stops_remaining_grouped,(df.trip_count - df.trip_num).clip(upper=1)
\ No newline at end of file
diff --git a/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs_purpose_stops.csv b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs_purpose_stops.csv
new file mode 100644
index 000000000..411be00eb
--- /dev/null
+++ b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs_purpose_stops.csv
@@ -0,0 +1,245 @@
+periods_left_min,periods_left_max,outbound,tour_purpose_grouped,half_tour_stops_remaining_grouped,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,All
+0,0,True,non_mand,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+0,0,True,non_mand,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
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diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_scheduling.yaml b/activitysim/examples/prototype_semcog/configs/joint_tour_scheduling.yaml
old mode 100755
new mode 100644
diff --git a/activitysim/examples/prototype_semcog/configs/logging.yaml b/activitysim/examples/prototype_semcog/configs/logging.yaml
old mode 100755
new mode 100644
diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv
old mode 100755
new mode 100644
diff --git a/conda-environments/activitysim-dev-base.yml b/conda-environments/activitysim-dev-base.yml
index ac061b6ea..1e63b621d 100644
--- a/conda-environments/activitysim-dev-base.yml
+++ b/conda-environments/activitysim-dev-base.yml
@@ -16,11 +16,11 @@ dependencies:
 - python=3.9
 - pip
 - asv  # for benchmarking
-- black
+- black >= 22.0,<23
 - bump2version  # for making a release
 - coveralls
-- cytoolz >= 0.8.1
-- dask
+- cytoolz = 0.12.*
+- dask = 2023.3.*
 - descartes
 - filelock
 - fsspec
@@ -30,44 +30,45 @@ dependencies:
 - ipykernel  # so this env will appear in jupyter as a selection
 - isort
 - jupyterlab
-- larch >= 5.5.8
+- larch = 5.7.*
 - matplotlib
 - myst-parser  # allows markdown in sphinx
 - nbconvert
 - nbformat
-- numba >= 0.55.2
+- numba = 0.56.*
 - numexpr
-- numpy >= 1.16.1,<=1.21
+- numpy = 1.23.*
 - numpydoc
-- openmatrix >= 0.3.4.1
-- orca >= 1.6
-- pandas >= 1.1.0
+- openmatrix = 0.3.*
+- orca = 1.8
+- pandas = 1.4.*
+- platformdirs = 3.2.*
 - pre-commit
-- psutil >= 4.1
-- pyarrow >= 2.0
+- psutil = 5.9.*
+- pyarrow = 11.*
 - pycodestyle
-- pydantic
+- pydantic = 1.10.*
 - pydata-sphinx-theme
-- pyinstrument
-- pypyr
+- pyinstrument = 4.4
+- pypyr = 5.8.*
 - pytables >=3.5.1,<3.7
-- pytest
+- pytest = 7.2.*
 - pytest-cov
 - pytest-regressions
-- pyyaml >= 5.1
-- requests >= 2.7
-- rich
+- pyyaml = 6.*
+- requests = 2.28.*
+- rich = 13.3.*
 - ruby  # required for benchmarking pre-commit hooks
 - setuptools_scm
-- scikit-learn >= 1.1
+- scikit-learn = 1.2.*
 - simwrapper > 1.7
 - snakeviz  # for profiling
-- sphinx
-- sphinx_rtd_theme
-- sphinx-argparse
-- xarray >= 0.21
+- sphinx = 6.1.*
+- sphinx_rtd_theme = 1.2.*
+- sphinx-argparse = 0.4.*
+- xarray = 2023.2.*
 - xmle
-- zarr
+- zarr = 2.14.*
 
 - pip:
   - autodoc_pydantic
diff --git a/conda-environments/activitysim-dev.yml b/conda-environments/activitysim-dev.yml
index 40e03ec10..cac073df6 100644
--- a/conda-environments/activitysim-dev.yml
+++ b/conda-environments/activitysim-dev.yml
@@ -12,11 +12,11 @@ dependencies:
 - python=3.9
 - pip
 - asv  # for benchmarking
-- black
+- black >= 22.0,<23
 - bump2version  # for making a release
 - coveralls
-- cytoolz >= 0.8.1
-- dask
+- cytoolz = 0.12.*
+- dask = 2023.3.*
 - descartes
 - filelock
 - fsspec
@@ -26,45 +26,46 @@ dependencies:
 - ipykernel  # so this env will appear in jupyter as a selection
 - isort
 - jupyterlab
-- larch >= 5.5.8
+- larch = 5.7.*
 - matplotlib
 - myst-parser  # allows markdown in sphinx
 - nbconvert
 - nbformat
-- numba >= 0.56.4
+- numba = 0.56.*
 - numexpr
-- numpy >= 1.16.1
+- numpy = 1.23.*
 - numpydoc
-- openmatrix >= 0.3.4.1
-- orca >= 1.6
-- pandas >= 1.1.0
+- openmatrix = 0.3.*
+- orca = 1.8
+- pandas = 1.4.*
+- platformdirs = 3.2.*
 - pre-commit
-- psutil >= 4.1
-- pyarrow >= 2.0
+- psutil = 5.9.*
+- pyarrow = 11.*
 - pycodestyle
-- pydantic
+- pydantic = 1.10.*
 - pydata-sphinx-theme
-- pyinstrument
-- pypyr
+- pyinstrument = 4.4
+- pypyr = 5.8.*
 - pytables >=3.5.1,<3.7
-- pytest
+- pytest = 7.2.*
 - pytest-cov
 - pytest-regressions
-- pyyaml >= 5.1
-- requests >= 2.7
-- rich
+- pyyaml = 6.*
+- requests = 2.28.*
+- rich = 13.3.*
 - ruby  # required for benchmarking pre-commit hooks
 - setuptools_scm
-- scikit-learn >= 1.1
+- scikit-learn = 1.2.*
 - sharrow >= 2.5.2
 - simwrapper > 1.7
 - snakeviz  # for profiling
-- sphinx
-- sphinx_rtd_theme
-- sphinx-argparse
-- xarray >= 0.21
+- sphinx = 6.1.*
+- sphinx_rtd_theme = 1.2.*
+- sphinx-argparse = 0.4.*
+- xarray = 2023.2.*
 - xmle
-- zarr
+- zarr = 2.14.*
 
 - pip:
   - autodoc_pydantic
diff --git a/conda-environments/docbuild.yml b/conda-environments/docbuild.yml
index 782eff73d..879338d1c 100644
--- a/conda-environments/docbuild.yml
+++ b/conda-environments/docbuild.yml
@@ -12,7 +12,7 @@ channels:
 dependencies:
 - python=3.9
 - pip
-- black
+- black >= 22.0,<23
 - bump2version
 - coveralls
 - cytoolz >= 0.8.1
diff --git a/conda-environments/github-actions-tests.yml b/conda-environments/github-actions-tests.yml
index f94ca7130..28d6c51f8 100644
--- a/conda-environments/github-actions-tests.yml
+++ b/conda-environments/github-actions-tests.yml
@@ -7,28 +7,29 @@ channels:
 - conda-forge
 dependencies:
 - pip
-- black
+- black >= 22.0,<23
 - coveralls
-- cytoolz >= 0.8.1
+- cytoolz = 0.12.*
+- dask = 2023.3.*
 - isort
 - nbmake
-- numba >= 0.56.4
-- numpy >= 1.16.1
-- openmatrix >= 0.3.4.1
-- orca >= 1.6
-- pandas >= 1.1.0
-- psutil >= 4.1
-- pyarrow >= 2.0
-- pypyr >= 5.3
+- numba = 0.56.*
+- numpy = 1.23.*
+- openmatrix = 0.3.*
+- orca = 1.8
+- pandas = 1.4.*
+- platformdirs = 3.2.*
+- psutil = 5.9.*
+- pyarrow = 11.*
+- pypyr = 5.8.*
 - pytables >= 3.5.1,<3.7  # orca's constraint
-- pytest
+- pytest = 7.2.*
 - pytest-cov
 - pytest-regressions
-- pyyaml >= 5.1
-- requests >= 2.7
-- scikit-learn >= 1.1
+- pyyaml = 6.*
+- requests = 2.28.*
+- scikit-learn = 1.2.*
 - sharrow >= 2.5.2
 - simwrapper > 1.7
-- scikit-learn >= 1.1.1
-- xarray >= 0.21
-- zarr
+- xarray = 2023.2.*
+- zarr = 2.14.*
diff --git a/docs/examples.rst b/docs/examples.rst
index add56f8b3..0b9634d28 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -290,7 +290,7 @@ After the frequency and time-of-day for work and school tours are determined, th
 next major model component relates to joint household travel. This component produces a
 number of joint tours by travel purpose for the entire household, travel party composition
 in terms of adults and children, and then defines the participation of each household
-member in each joint household tour. It is followed by choice of destination and time-ofday.
+member in each joint household tour. It is followed by choice of destination and time-of-day.
 
 The next stage relates to maintenance and discretionary tours that are modeled at the individual
 person level. The models include tour frequency, choice of destination and time
@@ -513,7 +513,6 @@ Configuration
 
 This section has been moved to :ref:`configuration`.
 
-
 .. _sub-model-spec-files:
 
 Sub-Model Specification Files
diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst
index e3eee6088..69ed9392d 100644
--- a/docs/gettingstarted.rst
+++ b/docs/gettingstarted.rst
@@ -24,7 +24,7 @@ installs a variety of things on your system, and it is quite likely to be flagge
 Windows, anti-virus, or institutional IT policies as "unusual" software, which
 may require special treatment to actually install and use.
 
-Download the installer from GitHub `here <https://github.com/ActivitySim/activitysim/releases/download/v1.2.0/Activitysim-1.2.0-Windows-x86_64.exe`__.
+Download the installer from GitHub `here <https://github.com/ActivitySim/activitysim/releases/download/v1.2.0/Activitysim-1.2.0-Windows-x86_64.exe>`__.
 It is strongly recommended to choose the option to install "for me only", as this
 should not require administrator privileges on your machine.  Pay attention
 to the *complete path* of the installation location. You will need to know
@@ -190,8 +190,14 @@ ActivitySim includes a :ref:`cli` for creating examples and running the model.
 To setup and run the primary example (see :ref:`examples`), do the following:
 
 * Open a command prompt
-* Activate the conda environment with ActivitySim installed (i.e. ``conda activate asim``)
-* Type ``activitysim create -e prototype_mtc -d test_prototype_mtc`` to copy the very small prototype_mtc example to a new test_prototype_mtc directory
+* If you installed ActivitySim using conda environments, activate the conda
+  environment with ActivitySim installed (i.e. ``conda activate asim``)
+* Or, if you used the :ref:`pre-packaged installer<Pre-packaged Installer>`,
+  replace all the commands below that call ``activitysim ...`` with the complete
+  path to your installed location, which is probably something
+  like ``c:\programdata\activitysim\scripts\activitysim.exe``.
+* Type ``activitysim create -e prototype_mtc -d test_prototype_mtc`` to copy
+  the very small prototype_mtc example to a new test_prototype_mtc directory
 * Change to the test_prototype_mtc directory
 * Type ``activitysim run -c configs -o output -d data`` to run the example
 * Review the outputs in the output directory
diff --git a/docs/models.rst b/docs/models.rst
index ef79a38cd..d7249a353 100644
--- a/docs/models.rst
+++ b/docs/models.rst
@@ -1211,8 +1211,8 @@ Core Table: ``trips`` | Result Field: ``purpose, destination`` | Skims Keys: ``o
 
 .. _trip_scheduling:
 
-Trip Scheduling (Probablistic)
-------------------------------
+Trip Scheduling (Probabilistic)
+-------------------------------
 
 For each trip, assign a departure hour based on an input lookup table of percents by tour purpose,
 direction (inbound/outbound), tour hour, and trip index.
@@ -1232,6 +1232,41 @@ The trip scheduling model does not use mode choice logsums.
 Alternatives: Available time periods in the tour window (i.e. tour start and end period).  When processing stops on
 work tours, the available time periods is constrained by the at-work subtour start and end period as well.
 
+In order to avoid trip failing, a new probabilistic trip scheduling mode was developed named "relative".
+When setting the _scheduling_mode_ option to relative, trips are scheduled relative to the previously scheduled trips.
+The first trip still departs when the tour starts and for every subsequent trip, the choices are selected with respect to
+the previous trip depart time. Inbound trips are no longer handled in reverse order.  The key to this relative mode is to
+index the probabilities based on how much time is remaining on the tour.  For tours that include subtours, the time remaining will
+be based on the subtour start time for outbound trips and will resume again for inbound trips after the subtour ends.
+By indexing the probabilities based on time remaining and scheduling relative to the previous trip, scheduling trips in relative
+mode will not fail.  Note also that relative scheduling mode requires the use of logic
+version 2 (see warning about logic versions, below).
+
+An example of trip scheduling in relative mode is included in the :ref:`prototype_mwcog` example.  In this example, trip
+scheduling probabilities are indexed by the following columns:
+  * periods_left_min: the minimum bin for the number of time periods left on the tour.
+  * periods_left_max: the maximum bin for the number of time periods left on the tour.  This is the same as periods_left_min until the final time period bin.
+  * outbound: whether the trip occurs on the outbound leg of a tour.
+  * tour_purpose_grouped: Tour purpose grouped into mandatory and non-mandatory categories
+  * half_tour_stops_remaining_grouped: The number of stops remaining on the half tour with the categories of 0 and 1+
+Each of these variables are listed as merge columns in the trip_scheduling.yaml file and are declared in the trip scheduling preprocessor.
+The variables above attempt to balance the statistics available for probability creation with the amount of segmentation of trip characteristics.
+
+.. warning::
+
+    Earlier versions of ActivitySim contained a logic error in this model, whereby
+    the earliest departure time for inbound legs was bounded by the maximum outbound
+    departure time, even if there was a scheduling failure for one or more outbound
+    leg departures and that bound was NA.  For continuity, this process has been
+    retained in this ActivitySim component as *logic_version* 1, and it remains the
+    default process if the user does not explicitly specify a logic version in the
+    model settings yaml file. The revised logic includes bounding inbound legs only
+    when the maximum outbound departure time is well defined.  This version of the
+    model can be used by explicitly setting `logic_version: 2` (or greater) in the
+    model settings yaml file.  It is strongly recommended that all new model
+    development efforts use logic version 2; a future version of ActivitySim may
+    make this the default for this component, and/or remove logic version 1 entirely.
+
 The main interface to the trip scheduling model is the
 :py:func:`~activitysim.abm.models.trip_scheduling.trip_scheduling` function.
 This function is registered as an Inject step in the example Pipeline.
diff --git a/setup.cfg b/setup.cfg
index cb3b41388..b2736a99a 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -29,7 +29,7 @@ install_requires =
     numpy >= 1.16.1
     openmatrix >= 0.3.4.1
     orca >= 1.6
-    pandas >= 1.1.0
+    pandas >= 1.4, <2
     psutil >= 4.1
     pyarrow >= 2.0
     pypyr >= 5.3
diff --git a/test/auto_ownership/.gitkeep b/test/auto_ownership/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/auto_ownership/configs/annotate_households.csv b/test/auto_ownership/configs/annotate_households.csv
new file mode 100644
index 000000000..ad2c67bab
--- /dev/null
+++ b/test/auto_ownership/configs/annotate_households.csv
@@ -0,0 +1,19 @@
+Description,Target,Expression
+#,, annotate households table after import
+,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
+number of non_workers,num_non_workers,households.hhsize - households.num_workers
+number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)"
+num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)"
+num_children,num_children,"_PERSON_COUNT('~adult', persons, households)"
+num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)"
+num_children_6_to_15,num_children_6_to_15,"_PERSON_COUNT('6 <= age <= 15', persons, households)"
+num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)"
+num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)"
+# get definition from travel model two
+num_young_adults,num_young_adults,"_PERSON_COUNT('18 <= age <= 24', persons, households)"
+num_young_retirees,num_young_retirees,"_PERSON_COUNT('65 <= age <= 79', persons, households)"
+num_old_retirees,num_old_retirees,"_PERSON_COUNT('80 <= age', persons, households)"
+num_persons_18_to_35,num_persons_18_to_35,"_PERSON_COUNT('18 <= age <= 35', persons, households)"
+num_persons_65_plus,num_persons_65_plus,"_PERSON_COUNT('65 <= age', persons, households)"
+num_highschool_graduates,num_highschool_graduates,"_PERSON_COUNT('9 <= education_attainment', persons, households)"
+#mobility av share, mobility_av_share, setting('mobility_av_share')
diff --git a/test/auto_ownership/configs/annotate_landuse.csv b/test/auto_ownership/configs/annotate_landuse.csv
new file mode 100644
index 000000000..65237c0ad
--- /dev/null
+++ b/test/auto_ownership/configs/annotate_landuse.csv
@@ -0,0 +1,6 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+household_density,household_density,land_use.TOTHH / land_use.TOTACRE
+employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE
+population_density,population_density,land_use.TOTPOP / land_use.TOTACRE
+density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
\ No newline at end of file
diff --git a/test/auto_ownership/configs/annotate_persons.csv b/test/auto_ownership/configs/annotate_persons.csv
new file mode 100644
index 000000000..d52946719
--- /dev/null
+++ b/test/auto_ownership/configs/annotate_persons.csv
@@ -0,0 +1,40 @@
+Description,Target,Expression
+#,, annotate persons table after import
+age_16_to_19,age_16_to_19,"persons.age.between(16, 19)"
+age_16_p,age_16_p,persons.age >= 16
+adult,adult,persons.age >= 18
+male,male,persons.sex == 1
+female,female,persons.sex == 2
+# TODO add ptype annotation
+#,ptype,
+#presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)"
+#presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)"
+#presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)"
+#presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)"
+#presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)"
+#presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)"
+#presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)"
+#presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)"
+#student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))"
+#nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))"
+#,,
+#,, FIXME - if person is a university student but has school age student category value then reset student category value
+#,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)"
+#,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student
+#,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)"
+#,, FIXME if student category is non-student and employment is student then reset student category value to student
+#,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)"
+#,,
+#is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])"
+#preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)"
+#preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)"
+#is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE)
+#is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE)
+#is_university,is_university,pstudent == PSTUDENT_UNIVERSITY
+#school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)"
+#school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)"
+#school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)"
+#,,
+#is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])"
+#,,
+home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)"
diff --git a/test/auto_ownership/configs/annotate_persons_after_hh.csv b/test/auto_ownership/configs/annotate_persons_after_hh.csv
new file mode 100644
index 000000000..0dfa16be6
--- /dev/null
+++ b/test/auto_ownership/configs/annotate_persons_after_hh.csv
@@ -0,0 +1,5 @@
+Description,Target,Expression
+#,, annotate persons table after annotate_households
+#,, adults get full hh_value_of_time and children get 60%
+,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)"
+,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)"
\ No newline at end of file
diff --git a/test/auto_ownership/configs/auto_ownership.csv b/test/auto_ownership/configs/auto_ownership.csv
new file mode 100644
index 000000000..4b8abdfa1
--- /dev/null
+++ b/test/auto_ownership/configs/auto_ownership.csv
@@ -0,0 +1,61 @@
+Label,Description,Expression,0_CARS,1_CAR_1CV,1_CAR_1AV,2_CARS_2CV,2_CARS_2AV,2_CARS_1CV1AV,3_CARS_3CV,3_CARS_3AV,3_CARS_2CV1AV,3_CARS_1CV2AV,4_CARS_4CV
+,Boolean indicating that household is in SF county,"@np.where(df.county_id == ID_SAN_FRANCISCO, 1, 0)",coef_boolean_indicating_that_household_is_in_sf_county_0_CARS,,,,,,,,,,
+,alternative specific constant for 1 driver household,@df.num_drivers==1,coef_alternative_specific_constant_for_1_driver_household_0_CARS,,,coef_alternative_specific_constant_for_1_driver_household_2_CARS_2CV,coef_alternative_specific_constant_for_1_driver_household_2_CARS_2AV,coef_alternative_specific_constant_for_1_driver_household_2_CARS_1CV1AV,coef_alternative_specific_constant_for_1_driver_household_3_CARS_3CV,coef_alternative_specific_constant_for_1_driver_household_3_CARS_3AV,coef_alternative_specific_constant_for_1_driver_household_3_CARS_2CV1AV,coef_alternative_specific_constant_for_1_driver_household_3_CARS_1CV2AV,coef_alternative_specific_constant_for_1_driver_household_4_CARS_4CV
+,alternative specific constant for 2 driver household,@df.num_drivers==2,coef_alternative_specific_constant_for_2_driver_household_0_CARS,coef_alternative_specific_constant_for_2_driver_household_1_CAR_1CV,coef_alternative_specific_constant_for_2_driver_household_1_CAR_1AV,,,,coef_alternative_specific_constant_for_2_driver_household_3_CARS_3CV,coef_alternative_specific_constant_for_2_driver_household_3_CARS_3AV,coef_alternative_specific_constant_for_2_driver_household_3_CARS_2CV1AV,coef_alternative_specific_constant_for_2_driver_household_3_CARS_1CV2AV,coef_alternative_specific_constant_for_2_driver_household_4_CARS_4CV
+,alternative specific constant for 3 driver household,@df.num_drivers==3,coef_alternative_specific_constant_for_3_driver_household_0_CARS,coef_alternative_specific_constant_for_3_driver_household_1_CAR_1CV,coef_alternative_specific_constant_for_3_driver_household_1_CAR_1AV,coef_alternative_specific_constant_for_3_driver_household_2_CARS_2CV,coef_alternative_specific_constant_for_3_driver_household_2_CARS_2AV,coef_alternative_specific_constant_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_alternative_specific_constant_for_3_driver_household_4_CARS_4CV
+,alternative specific constant for 4+ driver household,@df.num_drivers>=4,coef_alternative_specific_constant_for_4_driver_household_0_CARS,coef_alternative_specific_constant_for_4_driver_household_1_CAR_1CV,coef_alternative_specific_constant_for_4_driver_household_1_CAR_1AV,coef_alternative_specific_constant_for_4_driver_household_2_CARS_2CV,coef_alternative_specific_constant_for_4_driver_household_2_CARS_2AV,coef_alternative_specific_constant_for_4_driver_household_2_CARS_1CV1AV,coef_alternative_specific_constant_for_4_driver_household_3_CARS_3CV,coef_alternative_specific_constant_for_4_driver_household_3_CARS_3AV,coef_alternative_specific_constant_for_4_driver_household_3_CARS_2CV1AV,coef_alternative_specific_constant_for_4_driver_household_3_CARS_1CV2AV,
+,workers to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_workers/df.num_drivers), 0)",coef_workers_to_drivers_ratio_for_1_driver_household_0_CARS,,,coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,coef_workers_to_drivers_ratio_for_1_driver_household_4_CARS_4CV
+,workers to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_workers/df.num_drivers), 0)",coef_workers_to_drivers_ratio_for_2_driver_household_0_CARS,coef_workers_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_workers_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,coef_workers_to_drivers_ratio_for_2_driver_household_4_CARS_4CV
+,workers to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_workers/df.num_drivers), 0)",coef_workers_to_drivers_ratio_for_3_driver_household_0_CARS,coef_workers_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_workers_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_workers_to_drivers_ratio_for_3_driver_household_4_CARS_4CV
+,workers to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_workers/df.num_drivers), 0)",coef_workers_to_drivers_ratio_for_4_driver_household_0_CARS,coef_workers_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_workers_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,
+,young adults to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_young_adults/df.num_drivers), 0)",coef_young_adults_to_drivers_ratio_for_1_driver_household_0_CARS,,,,,,,,,,
+,young adults to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_young_adults/df.num_drivers), 0)",coef_young_adults_to_drivers_ratio_for_2_driver_household_0_CARS,coef_young_adults_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_young_adults_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,,,,,
+,young adults to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_young_adults/df.num_drivers), 0)",coef_young_adults_to_drivers_ratio_for_3_driver_household_0_CARS,coef_young_adults_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_young_adults_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,
+,young adults to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_young_adults/df.num_drivers), 0)",coef_young_adults_to_drivers_ratio_for_4_driver_household_0_CARS,coef_young_adults_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_young_adults_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,
+,school children to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_children_6_to_15/df.num_drivers), 0)",coef_school_children_to_drivers_ratio_for_1_driver_household_0_CARS,,,coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,coef_school_children_to_drivers_ratio_for_1_driver_household_4_CARS_4CV
+,school children to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_children_6_to_15/df.num_drivers), 0)",coef_school_children_to_drivers_ratio_for_2_driver_household_0_CARS,coef_school_children_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_school_children_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,coef_school_children_to_drivers_ratio_for_2_driver_household_4_CARS_4CV
+,school children to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_children_6_to_15/df.num_drivers), 0)",coef_school_children_to_drivers_ratio_for_3_driver_household_0_CARS,coef_school_children_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_school_children_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_school_children_to_drivers_ratio_for_3_driver_household_4_CARS_4CV
+,school children to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_children_6_to_15/df.num_drivers), 0)",coef_school_children_to_drivers_ratio_for_4_driver_household_0_CARS,coef_school_children_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_school_children_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,
+,young retirees to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_young_retirees/df.num_drivers), 0)",coef_young_retirees_to_drivers_ratio_for_1_driver_household_0_CARS,,,coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_4_CARS_4CV
+,young retirees to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_young_retirees/df.num_drivers), 0)",coef_young_retirees_to_drivers_ratio_for_2_driver_household_0_CARS,coef_young_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_4_CARS_4CV
+,young retirees to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_young_retirees/df.num_drivers), 0)",coef_young_retirees_to_drivers_ratio_for_3_driver_household_0_CARS,coef_young_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_young_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_young_retirees_to_drivers_ratio_for_3_driver_household_4_CARS_4CV
+,young retirees to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_young_retirees/df.num_drivers), 0)",coef_young_retirees_to_drivers_ratio_for_4_driver_household_0_CARS,coef_young_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,
+,old retirees to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_old_retirees/df.num_drivers), 0)",coef_old_retirees_to_drivers_ratio_for_1_driver_household_0_CARS,,,coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_4_CARS_4CV
+,old retirees to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_old_retirees/df.num_drivers), 0)",coef_old_retirees_to_drivers_ratio_for_2_driver_household_0_CARS,coef_old_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_4_CARS_4CV
+,old retirees to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_old_retirees/df.num_drivers), 0)",coef_old_retirees_to_drivers_ratio_for_3_driver_household_0_CARS,coef_old_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_old_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_old_retirees_to_drivers_ratio_for_3_driver_household_4_CARS_4CV
+,old retirees to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_old_retirees/df.num_drivers), 0)",coef_old_retirees_to_drivers_ratio_for_4_driver_household_0_CARS,coef_old_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,
+,household income <30k,@(df.income<30000),coef_household_income_30k_0_CARS,coef_household_income_30k_1_CAR_1CV,coef_household_income_30k_1_CAR_1AV,,,,coef_household_income_30k_3_CARS_3CV,coef_household_income_30k_3_CARS_3AV,coef_household_income_30k_3_CARS_2CV1AV,coef_household_income_30k_3_CARS_1CV2AV,coef_household_income_30k_4_CARS_4CV
+,household income 30-60k,@((df.income>=30000) & (df.income<60000)),coef_household_income_3060k_0_CARS,coef_household_income_3060k_1_CAR_1CV,coef_household_income_3060k_1_CAR_1AV,,,,coef_household_income_3060k_3_CARS_3CV,coef_household_income_3060k_3_CARS_3AV,coef_household_income_3060k_3_CARS_2CV1AV,coef_household_income_3060k_3_CARS_1CV2AV,coef_household_income_3060k_4_CARS_4CV
+,household income 100k+,@(df.income>=100000),coef_household_income_100k_0_CARS,coef_household_income_100k_1_CAR_1CV,coef_household_income_100k_1_CAR_1AV,,,,coef_household_income_100k_3_CARS_3CV,coef_household_income_100k_3_CARS_3AV,coef_household_income_100k_3_CARS_2CV1AV,coef_household_income_100k_3_CARS_1CV2AV,coef_household_income_100k_4_CARS_4CV
+,low educational level for 1 driver household,@((df.num_drivers==1) & (df.num_highschool_graduates==0)),coef_low_educational_level_for_1_driver_household_0_CARS,,,,,,coef_low_educational_level_for_1_driver_household_3_CARS_3CV,coef_low_educational_level_for_1_driver_household_3_CARS_3AV,coef_low_educational_level_for_1_driver_household_3_CARS_2CV1AV,coef_low_educational_level_for_1_driver_household_3_CARS_1CV2AV,coef_low_educational_level_for_1_driver_household_4_CARS_4CV
+,low educational level for 2 driver household,@((df.num_drivers==2) & (df.num_highschool_graduates==0)),coef_low_educational_level_for_2_driver_household_0_CARS,coef_low_educational_level_for_2_driver_household_1_CAR_1CV,coef_low_educational_level_for_2_driver_household_1_CAR_1AV,,,,coef_low_educational_level_for_2_driver_household_3_CARS_3CV,coef_low_educational_level_for_2_driver_household_3_CARS_3AV,coef_low_educational_level_for_2_driver_household_3_CARS_2CV1AV,coef_low_educational_level_for_2_driver_household_3_CARS_1CV2AV,coef_low_educational_level_for_2_driver_household_4_CARS_4CV
+,low educational level for 3 driver household,@((df.num_drivers==3) & (df.num_highschool_graduates==0)),coef_low_educational_level_for_3_driver_household_0_CARS,coef_low_educational_level_for_3_driver_household_1_CAR_1CV,coef_low_educational_level_for_3_driver_household_1_CAR_1AV,,,,,,,,coef_low_educational_level_for_3_driver_household_4_CARS_4CV
+,low educational level for 4+ driver household,@((df.num_drivers>=4) & (df.num_highschool_graduates==0)),coef_low_educational_level_for_4_driver_household_0_CARS,coef_low_educational_level_for_4_driver_household_1_CAR_1CV,coef_low_educational_level_for_4_driver_household_1_CAR_1AV,,,,coef_low_educational_level_for_4_driver_household_3_CARS_3CV,coef_low_educational_level_for_4_driver_household_3_CARS_3AV,coef_low_educational_level_for_4_driver_household_3_CARS_2CV1AV,coef_low_educational_level_for_4_driver_household_3_CARS_1CV2AV,
+,detached single family house,@df.building_size == 2,coef_detached_single_family_house_0_CARS,coef_detached_single_family_house_1_CAR_1CV,coef_detached_single_family_house_1_CAR_1AV,,,,coef_detached_single_family_house_3_CARS_3CV,coef_detached_single_family_house_3_CARS_3AV,coef_detached_single_family_house_3_CARS_2CV1AV,coef_detached_single_family_house_3_CARS_1CV2AV,coef_detached_single_family_house_4_CARS_4CV
+,Intersection density,TotInt/502,,,,,,,coef_intersection_density_3_CARS_3CV,coef_intersection_density_3_CARS_3AV,coef_intersection_density_3_CARS_2CV1AV,coef_intersection_density_3_CARS_1CV2AV,coef_intersection_density_4_CARS_4CV
+,Population density,PopDen,coef_population_density_0_CARS,coef_population_density_1_CAR_1CV,coef_population_density_1_CAR_1AV,,,,,,,,
+,Retail density,RetEmpDen,coef_retail_density_0_CARS,coef_retail_density_1_CAR_1CV,coef_retail_density_1_CAR_1AV,,,,,,,,
+,Non-motorized zonal accessbility,@df.nonmandatory_nm_accessibility,coef_nonmotorized_zonal_accessbility_0_CARS,coef_nonmotorized_zonal_accessbility_1_CAR_1CV,coef_nonmotorized_zonal_accessbility_1_CAR_1AV,,,,coef_nonmotorized_zonal_accessbility_3_CARS_3CV,coef_nonmotorized_zonal_accessbility_3_CARS_3AV,coef_nonmotorized_zonal_accessbility_3_CARS_2CV1AV,coef_nonmotorized_zonal_accessbility_3_CARS_1CV2AV,coef_nonmotorized_zonal_accessbility_4_CARS_4CV
+,auto-transit zonal accessibility,@(df.nonmandatory_auto_accessibility - df.nonmandatory_transit_accessibility),coef_autotransit_zonal_accessibility_0_CARS,coef_autotransit_zonal_accessibility_1_CAR_1CV,coef_autotransit_zonal_accessibility_1_CAR_1AV,,,,,,,,
+#,worker mandatory tour auto dependency,@((setting('use_accessibility') == 1) & (##workersAutoDependency##)),coef_worker_mandatory_tour_auto_dependency_0_CARS,coef_worker_mandatory_tour_auto_dependency_1_CAR_1CV,coef_worker_mandatory_tour_auto_dependency_1_CAR_1AV,,,,coef_worker_mandatory_tour_auto_dependency_3_CARS_3CV,coef_worker_mandatory_tour_auto_dependency_3_CARS_3AV,coef_worker_mandatory_tour_auto_dependency_3_CARS_2CV1AV,coef_worker_mandatory_tour_auto_dependency_3_CARS_1CV2AV,coef_worker_mandatory_tour_auto_dependency_4_CARS_4CV
+#,student mandatory tour auto dependency,@((setting('use_accessibility') == 1) & (##studentsAutoDependency##)),coef_student_mandatory_tour_auto_dependency_0_CARS,coef_student_mandatory_tour_auto_dependency_1_CAR_1CV,coef_student_mandatory_tour_auto_dependency_1_CAR_1AV,,,,,,,,
+#,worker mandatory tour rail mode portion,@((setting('use_accessibility') == 1) & (##workersRailProportion##)),coef_worker_mandatory_tour_rail_mode_portion_0_CARS,coef_worker_mandatory_tour_rail_mode_portion_1_CAR_1CV,coef_worker_mandatory_tour_rail_mode_portion_1_CAR_1AV,,,,coef_worker_mandatory_tour_rail_mode_portion_3_CARS_3CV,coef_worker_mandatory_tour_rail_mode_portion_3_CARS_3AV,coef_worker_mandatory_tour_rail_mode_portion_3_CARS_2CV1AV,coef_worker_mandatory_tour_rail_mode_portion_3_CARS_1CV2AV,coef_worker_mandatory_tour_rail_mode_portion_4_CARS_4CV
+#,student mandatory tour rail mode portion,@((setting('use_accessibility') == 1) & (##studentsRailProportion##)),coef_student_mandatory_tour_rail_mode_portion_0_CARS,coef_student_mandatory_tour_rail_mode_portion_1_CAR_1CV,coef_student_mandatory_tour_rail_mode_portion_1_CAR_1AV,,,,,,,,
+,ASC adjustment for non-GQ PRE-AO,@df.hh_unit_type==1,,coef_asc_adjustment_for_nongq_preao_1_CAR_1CV,coef_asc_adjustment_for_nongq_preao_1_CAR_1AV,coef_asc_adjustment_for_nongq_preao_2_CARS_2CV,coef_asc_adjustment_for_nongq_preao_2_CARS_2AV,coef_asc_adjustment_for_nongq_preao_2_CARS_1CV1AV,coef_asc_adjustment_for_nongq_preao_3_CARS_3CV,coef_asc_adjustment_for_nongq_preao_3_CARS_3AV,coef_asc_adjustment_for_nongq_preao_3_CARS_2CV1AV,coef_asc_adjustment_for_nongq_preao_3_CARS_1CV2AV,coef_asc_adjustment_for_nongq_preao_4_CARS_4CV
+,ASC adjustment for non-GQ POST-AO,@((df.hh_unit_type==1) & (setting('use_accessibility') == 1)),,coef_asc_adjustment_for_nongq_postao_1_CAR_1CV,coef_asc_adjustment_for_nongq_postao_1_CAR_1AV,coef_asc_adjustment_for_nongq_postao_2_CARS_2CV,coef_asc_adjustment_for_nongq_postao_2_CARS_2AV,coef_asc_adjustment_for_nongq_postao_2_CARS_1CV1AV,coef_asc_adjustment_for_nongq_postao_3_CARS_3CV,coef_asc_adjustment_for_nongq_postao_3_CARS_3AV,coef_asc_adjustment_for_nongq_postao_3_CARS_2CV1AV,coef_asc_adjustment_for_nongq_postao_3_CARS_1CV2AV,coef_asc_adjustment_for_nongq_postao_4_CARS_4CV
+,adjustment for 2 workers in non-family 0-car household,@((df.hh_type>3) & (df.num_workers==2)),coef_adjustment_for_2_workers_in_nonfamily_0car_household_0_CARS,,,,,,,,,,
+,adjustment for 3 workers in non-family 0-car household,@((df.hh_type>3) & (df.num_workers==3)),coef_adjustment_for_3_workers_in_nonfamily_0car_household_0_CARS,,,,,,,,,,
+,adjustment for 4+ workers in non-family 0-car household,@((df.hh_type>3) & (df.num_workers>3)),coef_adjustment_for_4_workers_in_nonfamily_0car_household_0_CARS,,,,,,,,,,
+,adjustment for 4+ workers in family 0-car household,@((df.hh_type<=3) & (df.num_workers>3)),coef_adjustment_for_4_workers_in_family_0car_household_0_CARS,,,,,,,,,,
+,TM2 Round 2 ASC adjustment for 1 workers in 0-car household,@df.num_workers==1,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_4_CARS_4CV
+,TM2 Round 2 ASC adjustment for 2 workers in 0-car household,@df.num_workers==2,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_4_CARS_4CV
+,TM2 Round 2 ASC adjustment for 3+ workers in 0-car household,@df.num_workers>=3,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_4_CARS_4CV
+,TM2 Round 2 ASC adjustment for non-GQ PRE-AO,@df.hh_unit_type==1,coef_tm2_round_2_asc_adjustment_for_nongq_preao_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_4_CARS_4CV
+,TM2 Round 2 ASC adjustment for non-GQ POST-AO,@((df.hh_unit_type==1) & (setting('use_accessibility') == 1)),coef_tm2_round_2_asc_adjustment_for_nongq_postao_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_4_CARS_4CV
+,AV ASC - Household Income under $50k,@df.income<50000,,,coef_av_asc__household_income_under_50k_1_CAR_1AV,,coef_av_asc__household_income_under_50k_2_CARS_2AV,coef_av_asc__household_income_under_50k_2_CARS_1CV1AV,,coef_av_asc__household_income_under_50k_3_CARS_3AV,coef_av_asc__household_income_under_50k_3_CARS_2CV1AV,coef_av_asc__household_income_under_50k_3_CARS_1CV2AV,
+,AV ASC - Household Income 100k+,@df.income>=100000,,,coef_av_asc__household_income_100k_1_CAR_1AV,,coef_av_asc__household_income_100k_2_CARS_2AV,coef_av_asc__household_income_100k_2_CARS_1CV1AV,,coef_av_asc__household_income_100k_3_CARS_3AV,coef_av_asc__household_income_100k_3_CARS_2CV1AV,coef_av_asc__household_income_100k_3_CARS_1CV2AV,
+,AV ASC - Young household,@(df.num_persons_18_to_35>=df.num_persons_65_plus),,,coef_av_asc__young_household_1_CAR_1AV,,coef_av_asc__young_household_2_CARS_2AV,coef_av_asc__young_household_2_CARS_1CV1AV,,coef_av_asc__young_household_3_CARS_3AV,coef_av_asc__young_household_3_CARS_2CV1AV,coef_av_asc__young_household_3_CARS_1CV2AV,
+,AV ASC - Old household,@(df.num_persons_18_to_35 < df.num_persons_65_plus),,,coef_av_asc__old_household_1_CAR_1AV,,coef_av_asc__old_household_2_CARS_2AV,coef_av_asc__old_household_2_CARS_1CV1AV,,coef_av_asc__old_household_3_CARS_3AV,coef_av_asc__old_household_3_CARS_2CV1AV,coef_av_asc__old_household_3_CARS_1CV2AV,
+#,"AV  Coeff Per Hour of travel by auto for work, summed across all workers in household",@((setting('use_accessibility') == 1) & (##@workAutoTime##)),,,"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_1_CAR_1AV",,"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_2_CARS_2AV","coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_2_CARS_1CV1AV",,"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_3AV","coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_2CV1AV","coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_1CV2AV",
+,AV ASC - 100% AV Share,@setting('mobility_av_share')==1.0,,coef_unavailable,,coef_unavailable,,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable
+,AV ASC - 0% AV Share,@setting('mobility_av_share')==0,,,coef_unavailable,,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,
diff --git a/test/auto_ownership/configs/auto_ownership.yaml b/test/auto_ownership/configs/auto_ownership.yaml
new file mode 100644
index 000000000..2cdde0464
--- /dev/null
+++ b/test/auto_ownership/configs/auto_ownership.yaml
@@ -0,0 +1,53 @@
+
+SPEC: auto_ownership.csv
+COEFFICIENTS: auto_ownership_coefficients.csv
+
+LOGIT_TYPE: NL
+
+NESTS:
+  name: root
+  coefficient: 1
+  alternatives:
+    - name: own no car
+      coefficient: 0.668
+      alternatives:
+        - name: zero car
+          coefficient: 0.3
+          alternatives:
+            - 0_CARS
+    - name: own cars
+      coefficient: 0.668
+      alternatives:
+        - name: one car
+          coefficient: 0.3
+          alternatives:
+            - 1_CAR_1CV
+            - 1_CAR_1AV
+        - name: two cars
+          coefficient: 0.3
+          alternatives:
+            - 2_CARS_2CV
+            - 2_CARS_2AV
+            - 2_CARS_1CV1AV
+        - name: three cars
+          coefficient: 0.3
+          alternatives:
+            - 3_CARS_3CV
+            - 3_CARS_3AV
+            - 3_CARS_2CV1AV
+            - 3_CARS_1CV2AV
+        - name: four cars
+          coefficient: 0.3
+          alternatives:
+            - 4_CARS_4CV
+
+CONSTANTS:
+  ID_SAN_FRANCISCO: 1
+  ID_SAN_MATEO: 2
+  ID_SANTA_CLARA: 3
+  ID_ALAMEDA: 4
+  ID_CONTRA_COSTA: 5
+  ID_SOLANO: 6
+  ID_NAPA: 7
+  ID_SONOMA: 8
+  ID_MARIN: 9
diff --git a/test/auto_ownership/configs/auto_ownership_coefficients.csv b/test/auto_ownership/configs/auto_ownership_coefficients.csv
new file mode 100644
index 000000000..1d88703f1
--- /dev/null
+++ b/test/auto_ownership/configs/auto_ownership_coefficients.csv
@@ -0,0 +1,393 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_boolean_indicating_that_household_is_in_sf_county_0_CARS,1.233276051,F
+coef_alternative_specific_constant_for_1_driver_household_0_CARS,-1.920143842,F
+coef_alternative_specific_constant_for_2_driver_household_0_CARS,-3.470781624,F
+coef_alternative_specific_constant_for_3_driver_household_0_CARS,-3.329633459,F
+coef_alternative_specific_constant_for_4_driver_household_0_CARS,-6.111494495,F
+coef_workers_to_drivers_ratio_for_1_driver_household_0_CARS,-1.464256557,F
+coef_workers_to_drivers_ratio_for_2_driver_household_0_CARS,-1.464256557,F
+coef_workers_to_drivers_ratio_for_3_driver_household_0_CARS,-1.464256557,F
+coef_workers_to_drivers_ratio_for_4_driver_household_0_CARS,-1.464256557,F
+coef_young_adults_to_drivers_ratio_for_1_driver_household_0_CARS,-1.769767608,F
+coef_young_adults_to_drivers_ratio_for_2_driver_household_0_CARS,-1.769767608,F
+coef_young_adults_to_drivers_ratio_for_3_driver_household_0_CARS,-1.769767608,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_0_CARS,-1.769767608,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_0_CARS,-0.346415088,F
+coef_school_children_to_drivers_ratio_for_2_driver_household_0_CARS,-0.346415088,F
+coef_school_children_to_drivers_ratio_for_3_driver_household_0_CARS,-0.346415088,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_0_CARS,-0.346415088,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_0_CARS,-0.38172487,F
+coef_young_retirees_to_drivers_ratio_for_2_driver_household_0_CARS,-0.38172487,F
+coef_young_retirees_to_drivers_ratio_for_3_driver_household_0_CARS,-0.38172487,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_0_CARS,-0.38172487,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_0_CARS,0.608700053,F
+coef_old_retirees_to_drivers_ratio_for_2_driver_household_0_CARS,0.608700053,F
+coef_old_retirees_to_drivers_ratio_for_3_driver_household_0_CARS,0.608700053,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_0_CARS,0.608700053,F
+coef_household_income_30k_0_CARS,2.654926169,F
+coef_household_income_3060k_0_CARS,0.918219369,F
+coef_household_income_100k_0_CARS,-0.44306913,F
+coef_low_educational_level_for_1_driver_household_0_CARS,0.767760942,F
+coef_low_educational_level_for_2_driver_household_0_CARS,0.767760942,F
+coef_low_educational_level_for_3_driver_household_0_CARS,0.767760942,F
+coef_low_educational_level_for_4_driver_household_0_CARS,0.767760942,F
+coef_detached_single_family_house_0_CARS,-1.774689947,F
+coef_population_density_0_CARS,0.046206562,F
+coef_retail_density_0_CARS,0.074563803,F
+coef_nonmotorized_zonal_accessbility_0_CARS,0.060875253,F
+coef_autotransit_zonal_accessibility_0_CARS,-0.200776981,F
+coef_worker_mandatory_tour_auto_dependency_0_CARS,-0.16516642,F
+coef_student_mandatory_tour_auto_dependency_0_CARS,-0.04422711,F
+coef_worker_mandatory_tour_rail_mode_portion_0_CARS,0.171079447,F
+coef_student_mandatory_tour_rail_mode_portion_0_CARS,0.26285636,F
+coef_adjustment_for_2_workers_in_nonfamily_0car_household_0_CARS,0.725034403,F
+coef_adjustment_for_3_workers_in_nonfamily_0car_household_0_CARS,1.194820674,F
+coef_adjustment_for_4_workers_in_nonfamily_0car_household_0_CARS,2.844226409,F
+coef_adjustment_for_4_workers_in_family_0car_household_0_CARS,2.116557339,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_0_CARS,1.097234124,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_0_CARS,0.893156568,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_0_CARS,0.937133013,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_0_CARS,-1.511025686,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_0_CARS,-0.115788174,F
+coef_alternative_specific_constant_for_2_driver_household_1_CAR_1CV,-2.20339718,F
+coef_alternative_specific_constant_for_3_driver_household_1_CAR_1CV,-2.473061365,F
+coef_alternative_specific_constant_for_4_driver_household_1_CAR_1CV,-3.983965837,F
+coef_workers_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,-0.407798013,F
+coef_workers_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,-0.407798013,F
+coef_workers_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,-0.598263093,F
+coef_young_adults_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,-0.598263093,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,-0.018572553,F
+coef_school_children_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,-0.018572553,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,-0.19861915,F
+coef_young_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,-0.19861915,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,0.232672708,F
+coef_old_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,0.232672708,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,0.232672708,F
+coef_household_income_30k_1_CAR_1CV,0.632172646,F
+coef_household_income_3060k_1_CAR_1CV,0.342605571,F
+coef_household_income_100k_1_CAR_1CV,-0.44306913,F
+coef_low_educational_level_for_2_driver_household_1_CAR_1CV,0.432294097,F
+coef_low_educational_level_for_3_driver_household_1_CAR_1CV,0.432294097,F
+coef_low_educational_level_for_4_driver_household_1_CAR_1CV,0.432294097,F
+coef_detached_single_family_house_1_CAR_1CV,-0.528137281,F
+coef_population_density_1_CAR_1CV,0.02268226,F
+coef_retail_density_1_CAR_1CV,0.029544267,F
+coef_nonmotorized_zonal_accessbility_1_CAR_1CV,0.039970159,F
+coef_autotransit_zonal_accessibility_1_CAR_1CV,-0.009060243,F
+coef_worker_mandatory_tour_auto_dependency_1_CAR_1CV,-0.16516642,F
+coef_student_mandatory_tour_auto_dependency_1_CAR_1CV,-0.04422711,F
+coef_worker_mandatory_tour_rail_mode_portion_1_CAR_1CV,0.171079447,F
+coef_student_mandatory_tour_rail_mode_portion_1_CAR_1CV,0.26285636,F
+coef_asc_adjustment_for_nongq_preao_1_CAR_1CV,0.04582774,F
+coef_asc_adjustment_for_nongq_postao_1_CAR_1CV,-0.013942306,F
+coef_alternative_specific_constant_for_2_driver_household_1_CAR_1AV,-2.20339718,F
+coef_alternative_specific_constant_for_3_driver_household_1_CAR_1AV,-2.473061365,F
+coef_alternative_specific_constant_for_4_driver_household_1_CAR_1AV,-3.983965837,F
+coef_workers_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,-0.407798013,F
+coef_workers_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,-0.407798013,F
+coef_workers_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,-0.598263093,F
+coef_young_adults_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,-0.598263093,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,-0.018572553,F
+coef_school_children_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,-0.018572553,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,-0.19861915,F
+coef_young_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,-0.19861915,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,0.232672708,F
+coef_old_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,0.232672708,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,0.232672708,F
+coef_household_income_30k_1_CAR_1AV,0.632172646,F
+coef_household_income_3060k_1_CAR_1AV,0.342605571,F
+coef_household_income_100k_1_CAR_1AV,-0.44306913,F
+coef_low_educational_level_for_2_driver_household_1_CAR_1AV,0.432294097,F
+coef_low_educational_level_for_3_driver_household_1_CAR_1AV,0.432294097,F
+coef_low_educational_level_for_4_driver_household_1_CAR_1AV,0.432294097,F
+coef_detached_single_family_house_1_CAR_1AV,-0.528137281,F
+coef_population_density_1_CAR_1AV,0.02268226,F
+coef_retail_density_1_CAR_1AV,0.029544267,F
+coef_nonmotorized_zonal_accessbility_1_CAR_1AV,0.039970159,F
+coef_autotransit_zonal_accessibility_1_CAR_1AV,-0.009060243,F
+coef_worker_mandatory_tour_auto_dependency_1_CAR_1AV,-0.16516642,F
+coef_student_mandatory_tour_auto_dependency_1_CAR_1AV,-0.04422711,F
+coef_worker_mandatory_tour_rail_mode_portion_1_CAR_1AV,0.171079447,F
+coef_student_mandatory_tour_rail_mode_portion_1_CAR_1AV,0.26285636,F
+coef_asc_adjustment_for_nongq_preao_1_CAR_1AV,0.04582774,F
+coef_asc_adjustment_for_nongq_postao_1_CAR_1AV,-0.013942306,F
+coef_av_asc__household_income_under_50k_1_CAR_1AV,-1,F
+coef_av_asc__household_income_100k_1_CAR_1AV,1,F
+coef_av_asc__young_household_1_CAR_1AV,0.5,F
+coef_av_asc__old_household_1_CAR_1AV,-1,F
+"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_1_CAR_1AV",0.25,F
+coef_alternative_specific_constant_for_1_driver_household_2_CARS_2CV,-0.934889684,F
+coef_alternative_specific_constant_for_3_driver_household_2_CARS_2CV,-0.317496682,F
+coef_alternative_specific_constant_for_4_driver_household_2_CARS_2CV,-0.909626603,F
+coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,0.474996826,F
+coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,-0.407798013,F
+coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,-0.598263093,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,-0.018572553,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,-0.19861915,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,0.232672708,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,0.232672708,F
+coef_asc_adjustment_for_nongq_preao_2_CARS_2CV,-1.602468896,F
+coef_asc_adjustment_for_nongq_postao_2_CARS_2CV,-0.132328921,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_2CV,-0.151698573,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_2CV,-0.17099367,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_2CV,-0.553211417,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_2CV,0.723793144,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_2CV,0.16375467,F
+coef_alternative_specific_constant_for_1_driver_household_2_CARS_2AV,-0.934889684,F
+coef_alternative_specific_constant_for_3_driver_household_2_CARS_2AV,-0.317496682,F
+coef_alternative_specific_constant_for_4_driver_household_2_CARS_2AV,-0.909626603,F
+coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,0.474996826,F
+coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,-0.407798013,F
+coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,-0.598263093,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,-0.018572553,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,-0.19861915,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,0.232672708,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,0.232672708,F
+coef_asc_adjustment_for_nongq_preao_2_CARS_2AV,-1.602468896,F
+coef_asc_adjustment_for_nongq_postao_2_CARS_2AV,-0.132328921,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_2AV,-0.151698573,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_2AV,-0.17099367,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_2AV,-0.553211417,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_2AV,0.723793144,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_2AV,0.16375467,F
+coef_av_asc__household_income_under_50k_2_CARS_2AV,-1,F
+coef_av_asc__household_income_100k_2_CARS_2AV,1,F
+coef_av_asc__young_household_2_CARS_2AV,0.5,F
+coef_av_asc__old_household_2_CARS_2AV,-1,F
+"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_2_CARS_2AV",0.25,F
+coef_alternative_specific_constant_for_1_driver_household_2_CARS_1CV1AV,-0.934889684,F
+coef_alternative_specific_constant_for_3_driver_household_2_CARS_1CV1AV,-0.317496682,F
+coef_alternative_specific_constant_for_4_driver_household_2_CARS_1CV1AV,-0.909626603,F
+coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,0.474996826,F
+coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,-0.407798013,F
+coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,-0.598263093,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,-0.018572553,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,-0.19861915,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,0.232672708,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,0.232672708,F
+coef_asc_adjustment_for_nongq_preao_2_CARS_1CV1AV,-1.602468896,F
+coef_asc_adjustment_for_nongq_postao_2_CARS_1CV1AV,-0.132328921,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_1CV1AV,-0.151698573,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_1CV1AV,-0.17099367,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_1CV1AV,-0.553211417,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_1CV1AV,0.723793144,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_1CV1AV,0.16375467,F
+coef_av_asc__household_income_under_50k_2_CARS_1CV1AV,-1,F
+coef_av_asc__household_income_100k_2_CARS_1CV1AV,1,F
+coef_av_asc__young_household_2_CARS_1CV1AV,0.5,F
+coef_av_asc__old_household_2_CARS_1CV1AV,-1,F
+"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_2_CARS_1CV1AV",0.25,F
+coef_alternative_specific_constant_for_1_driver_household_3_CARS_3CV,-1.904733531,F
+coef_alternative_specific_constant_for_2_driver_household_3_CARS_3CV,-0.829354142,F
+coef_alternative_specific_constant_for_4_driver_household_3_CARS_3CV,-0.524427892,F
+coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,0.474996826,F
+coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,0.474996826,F
+coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,0.232672708,F
+coef_household_income_30k_3_CARS_3CV,-0.601152419,F
+coef_household_income_3060k_3_CARS_3CV,-0.305680609,F
+coef_household_income_100k_3_CARS_3CV,0.087029823,F
+coef_low_educational_level_for_1_driver_household_3_CARS_3CV,-0.176660377,F
+coef_low_educational_level_for_2_driver_household_3_CARS_3CV,-0.176660377,F
+coef_low_educational_level_for_4_driver_household_3_CARS_3CV,0.432294097,F
+coef_detached_single_family_house_3_CARS_3CV,0.427320086,F
+coef_intersection_density_3_CARS_3CV,-0.326525635,F
+coef_nonmotorized_zonal_accessbility_3_CARS_3CV,-0.052524462,F
+coef_worker_mandatory_tour_auto_dependency_3_CARS_3CV,0.104064535,F
+coef_worker_mandatory_tour_rail_mode_portion_3_CARS_3CV,-0.206713378,F
+coef_asc_adjustment_for_nongq_preao_3_CARS_3CV,-2.169791463,F
+coef_asc_adjustment_for_nongq_postao_3_CARS_3CV,-0.180285245,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_3CV,-0.209786195,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_3CV,-0.382492356,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_3CV,0.065128979,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_3CV,0.617090781,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_3CV,0.222715484,F
+coef_alternative_specific_constant_for_1_driver_household_3_CARS_3AV,-1.904733531,F
+coef_alternative_specific_constant_for_2_driver_household_3_CARS_3AV,-0.829354142,F
+coef_alternative_specific_constant_for_4_driver_household_3_CARS_3AV,-0.524427892,F
+coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,0.474996826,F
+coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,0.474996826,F
+coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,0.232672708,F
+coef_household_income_30k_3_CARS_3AV,-0.601152419,F
+coef_household_income_3060k_3_CARS_3AV,-0.305680609,F
+coef_household_income_100k_3_CARS_3AV,0.087029823,F
+coef_low_educational_level_for_1_driver_household_3_CARS_3AV,-0.176660377,F
+coef_low_educational_level_for_2_driver_household_3_CARS_3AV,-0.176660377,F
+coef_low_educational_level_for_4_driver_household_3_CARS_3AV,0.432294097,F
+coef_detached_single_family_house_3_CARS_3AV,0.427320086,F
+coef_intersection_density_3_CARS_3AV,-0.326525635,F
+coef_nonmotorized_zonal_accessbility_3_CARS_3AV,-0.052524462,F
+coef_worker_mandatory_tour_auto_dependency_3_CARS_3AV,0.104064535,F
+coef_worker_mandatory_tour_rail_mode_portion_3_CARS_3AV,-0.206713378,F
+coef_asc_adjustment_for_nongq_preao_3_CARS_3AV,-2.169791463,F
+coef_asc_adjustment_for_nongq_postao_3_CARS_3AV,-0.180285245,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_3AV,-0.209786195,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_3AV,-0.382492356,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_3AV,0.065128979,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_3AV,0.617090781,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_3AV,0.222715484,F
+coef_av_asc__household_income_under_50k_3_CARS_3AV,-1,F
+coef_av_asc__household_income_100k_3_CARS_3AV,1,F
+coef_av_asc__young_household_3_CARS_3AV,0.5,F
+coef_av_asc__old_household_3_CARS_3AV,-1,F
+"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_3AV",0.25,F
+coef_alternative_specific_constant_for_1_driver_household_3_CARS_2CV1AV,-1.904733531,F
+coef_alternative_specific_constant_for_2_driver_household_3_CARS_2CV1AV,-0.829354142,F
+coef_alternative_specific_constant_for_4_driver_household_3_CARS_2CV1AV,-0.524427892,F
+coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,0.474996826,F
+coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,0.474996826,F
+coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,0.232672708,F
+coef_household_income_30k_3_CARS_2CV1AV,-0.601152419,F
+coef_household_income_3060k_3_CARS_2CV1AV,-0.305680609,F
+coef_household_income_100k_3_CARS_2CV1AV,0.087029823,F
+coef_low_educational_level_for_1_driver_household_3_CARS_2CV1AV,-0.176660377,F
+coef_low_educational_level_for_2_driver_household_3_CARS_2CV1AV,-0.176660377,F
+coef_low_educational_level_for_4_driver_household_3_CARS_2CV1AV,0.432294097,F
+coef_detached_single_family_house_3_CARS_2CV1AV,0.427320086,F
+coef_intersection_density_3_CARS_2CV1AV,-0.326525635,F
+coef_nonmotorized_zonal_accessbility_3_CARS_2CV1AV,-0.052524462,F
+coef_worker_mandatory_tour_auto_dependency_3_CARS_2CV1AV,0.104064535,F
+coef_worker_mandatory_tour_rail_mode_portion_3_CARS_2CV1AV,-0.206713378,F
+coef_asc_adjustment_for_nongq_preao_3_CARS_2CV1AV,-2.169791463,F
+coef_asc_adjustment_for_nongq_postao_3_CARS_2CV1AV,-0.180285245,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_2CV1AV,-0.209786195,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_2CV1AV,-0.382492356,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_2CV1AV,0.065128979,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_2CV1AV,0.617090781,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_2CV1AV,0.222715484,F
+coef_av_asc__household_income_under_50k_3_CARS_2CV1AV,-1,F
+coef_av_asc__household_income_100k_3_CARS_2CV1AV,1,F
+coef_av_asc__young_household_3_CARS_2CV1AV,0.5,F
+coef_av_asc__old_household_3_CARS_2CV1AV,-1,F
+"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_2CV1AV",0.25,F
+coef_alternative_specific_constant_for_1_driver_household_3_CARS_1CV2AV,-1.904733531,F
+coef_alternative_specific_constant_for_2_driver_household_3_CARS_1CV2AV,-0.829354142,F
+coef_alternative_specific_constant_for_4_driver_household_3_CARS_1CV2AV,-0.524427892,F
+coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,0.474996826,F
+coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,0.474996826,F
+coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,-0.407798013,F
+coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,-0.598263093,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,-0.018572553,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,-0.19861915,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,0.232672708,F
+coef_household_income_30k_3_CARS_1CV2AV,-0.601152419,F
+coef_household_income_3060k_3_CARS_1CV2AV,-0.305680609,F
+coef_household_income_100k_3_CARS_1CV2AV,0.087029823,F
+coef_low_educational_level_for_1_driver_household_3_CARS_1CV2AV,-0.176660377,F
+coef_low_educational_level_for_2_driver_household_3_CARS_1CV2AV,-0.176660377,F
+coef_low_educational_level_for_4_driver_household_3_CARS_1CV2AV,0.432294097,F
+coef_detached_single_family_house_3_CARS_1CV2AV,0.427320086,F
+coef_intersection_density_3_CARS_1CV2AV,-0.326525635,F
+coef_nonmotorized_zonal_accessbility_3_CARS_1CV2AV,-0.052524462,F
+coef_worker_mandatory_tour_auto_dependency_3_CARS_1CV2AV,0.104064535,F
+coef_worker_mandatory_tour_rail_mode_portion_3_CARS_1CV2AV,-0.206713378,F
+coef_asc_adjustment_for_nongq_preao_3_CARS_1CV2AV,-2.169791463,F
+coef_asc_adjustment_for_nongq_postao_3_CARS_1CV2AV,-0.180285245,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_1CV2AV,-0.209786195,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_1CV2AV,-0.382492356,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_1CV2AV,0.065128979,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_1CV2AV,0.617090781,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_1CV2AV,0.222715484,F
+coef_av_asc__household_income_under_50k_3_CARS_1CV2AV,-1,F
+coef_av_asc__household_income_100k_3_CARS_1CV2AV,1,F
+coef_av_asc__young_household_3_CARS_1CV2AV,0.5,F
+coef_av_asc__old_household_3_CARS_1CV2AV,-1,F
+"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_1CV2AV",0.25,F
+coef_alternative_specific_constant_for_1_driver_household_4_CARS_4CV,-2.488003709,F
+coef_alternative_specific_constant_for_2_driver_household_4_CARS_4CV,-1.778311581,F
+coef_alternative_specific_constant_for_3_driver_household_4_CARS_4CV,-0.533915593,F
+coef_workers_to_drivers_ratio_for_1_driver_household_4_CARS_4CV,0.474996826,F
+coef_workers_to_drivers_ratio_for_2_driver_household_4_CARS_4CV,0.474996826,F
+coef_workers_to_drivers_ratio_for_3_driver_household_4_CARS_4CV,0.474996826,F
+coef_school_children_to_drivers_ratio_for_1_driver_household_4_CARS_4CV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_2_driver_household_4_CARS_4CV,0.108758563,F
+coef_school_children_to_drivers_ratio_for_3_driver_household_4_CARS_4CV,0.108758563,F
+coef_young_retirees_to_drivers_ratio_for_1_driver_household_4_CARS_4CV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_2_driver_household_4_CARS_4CV,0.28676361,F
+coef_young_retirees_to_drivers_ratio_for_3_driver_household_4_CARS_4CV,0.28676361,F
+coef_old_retirees_to_drivers_ratio_for_1_driver_household_4_CARS_4CV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_2_driver_household_4_CARS_4CV,-0.901020484,F
+coef_old_retirees_to_drivers_ratio_for_3_driver_household_4_CARS_4CV,-0.901020484,F
+coef_household_income_30k_4_CARS_4CV,-0.936061162,F
+coef_household_income_3060k_4_CARS_4CV,-0.271548734,F
+coef_household_income_100k_4_CARS_4CV,0.144123363,F
+coef_low_educational_level_for_1_driver_household_4_CARS_4CV,-0.176660377,F
+coef_low_educational_level_for_2_driver_household_4_CARS_4CV,-0.176660377,F
+coef_low_educational_level_for_3_driver_household_4_CARS_4CV,-0.176660377,F
+coef_detached_single_family_house_4_CARS_4CV,0.539511974,F
+coef_intersection_density_4_CARS_4CV,-0.326525635,F
+coef_nonmotorized_zonal_accessbility_4_CARS_4CV,-0.100612397,F
+coef_worker_mandatory_tour_auto_dependency_4_CARS_4CV,0.104064535,F
+coef_worker_mandatory_tour_rail_mode_portion_4_CARS_4CV,-0.206713378,F
+coef_asc_adjustment_for_nongq_preao_4_CARS_4CV,-2.641133846,F
+coef_asc_adjustment_for_nongq_postao_4_CARS_4CV,-0.186095557,F
+coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_4_CARS_4CV,-0.308288035,F
+coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_4_CARS_4CV,-0.551034746,F
+coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_4_CARS_4CV,-0.048670057,F
+coef_tm2_round_2_asc_adjustment_for_nongq_preao_4_CARS_4CV,0.69421287,F
+coef_tm2_round_2_asc_adjustment_for_nongq_postao_4_CARS_4CV,0.2639107,F
diff --git a/test/auto_ownership/configs/constants.yaml b/test/auto_ownership/configs/constants.yaml
new file mode 100644
index 000000000..b0bd5a1f3
--- /dev/null
+++ b/test/auto_ownership/configs/constants.yaml
@@ -0,0 +1,68 @@
+## ActivitySim
+## See full license in LICENSE.txt.
+
+
+#HHT_NONE: 0
+#HHT_FAMILY_MARRIED: 1
+#HHT_FAMILY_MALE: 2
+#HHT_FAMILY_FEMALE: 3
+#HHT_NONFAMILY_MALE_ALONE: 4
+#HHT_NONFAMILY_MALE_NOTALONE: 5
+#HHT_NONFAMILY_FEMALE_ALONE: 6
+#HHT_NONFAMILY_FEMALE_NOTALONE: 7
+
+# convenience for expression files
+HHT_NONFAMILY: [4, 5, 6, 7]
+HHT_FAMILY: [1, 2, 3]
+
+PSTUDENT_GRADE_OR_HIGH: 1
+PSTUDENT_UNIVERSITY: 2
+PSTUDENT_NOT: 3
+
+GRADE_SCHOOL_MAX_AGE: 14
+GRADE_SCHOOL_MIN_AGE: 5
+
+SCHOOL_SEGMENT_NONE: 0
+SCHOOL_SEGMENT_GRADE: 1
+SCHOOL_SEGMENT_HIGH: 2
+SCHOOL_SEGMENT_UNIV: 3
+
+INCOME_SEGMENT_LOW: 1
+INCOME_SEGMENT_MED: 2
+INCOME_SEGMENT_HIGH: 3
+INCOME_SEGMENT_VERYHIGH: 4
+
+PEMPLOY_FULL: 1
+PEMPLOY_PART: 2
+PEMPLOY_NOT: 3
+PEMPLOY_CHILD: 4
+
+PTYPE_FULL: &ptype_full 1
+PTYPE_PART: &ptype_part 2
+PTYPE_UNIVERSITY: &ptype_university 3
+PTYPE_NONWORK: &ptype_nonwork 4
+PTYPE_RETIRED: &ptype_retired 5
+PTYPE_DRIVING: &ptype_driving 6
+PTYPE_SCHOOL: &ptype_school 7
+PTYPE_PRESCHOOL:  &ptype_preschool 8
+
+# these appear as column headers in non_mandatory_tour_frequency.csv
+PTYPE_NAME:
+  *ptype_full: PTYPE_FULL
+  *ptype_part: PTYPE_PART
+  *ptype_university: PTYPE_UNIVERSITY
+  *ptype_nonwork: PTYPE_NONWORK
+  *ptype_retired: PTYPE_RETIRED
+  *ptype_driving: PTYPE_DRIVING
+  *ptype_school: PTYPE_SCHOOL
+  *ptype_preschool: PTYPE_PRESCHOOL
+
+
+CDAP_ACTIVITY_MANDATORY: M
+CDAP_ACTIVITY_NONMANDATORY: N
+CDAP_ACTIVITY_HOME: H
+
+# Correction for transit skim expressions
+# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and
+# therefore need to be divided by the scale factor if used in expressions
+TRANSIT_SCALE_FACTOR: 100
diff --git a/test/auto_ownership/configs/initialize_households.yaml b/test/auto_ownership/configs/initialize_households.yaml
new file mode 100644
index 000000000..5d98a703d
--- /dev/null
+++ b/test/auto_ownership/configs/initialize_households.yaml
@@ -0,0 +1,37 @@
+annotate_tables:
+  - tablename: persons
+    column_map:
+      HHID: household_id
+      PERID: person_id
+      AGEP: age
+      SEX: sex
+      SCHL: education_attainment
+      OCCP: occupation
+      WKHP: hours_worked
+      WKW: weeks_worked
+      EMPLOYED: employment_status
+      ESR: esr
+      SCHG: grade_attending
+    annotate:
+      SPEC: annotate_persons
+      DF: persons
+      TABLES:
+        - households
+  - tablename: households
+    column_map:
+      HHID: household_id
+      MAZ: home_zone_id
+      HHINCADJ: income
+      NWRKRS_ESR: num_workers
+      VEH: auto_ownership
+      NP: hhsize
+      HHT: hh_type
+      BLD: building_size
+      TYPE: hh_unit_type
+      MTCCountyID: county_id
+    annotate:
+      SPEC: annotate_households
+      DF: households
+      TABLES:
+        - persons
+        - land_use
\ No newline at end of file
diff --git a/test/auto_ownership/configs/initialize_landuse.yaml b/test/auto_ownership/configs/initialize_landuse.yaml
new file mode 100644
index 000000000..7cc1d7e34
--- /dev/null
+++ b/test/auto_ownership/configs/initialize_landuse.yaml
@@ -0,0 +1,54 @@
+annotate_tables:
+  - tablename: land_use
+    column_map:
+        MAZ_ORIGINAL: zone_id
+        CountyID: county_id
+        DistID: DISTRICT
+        HH: TOTHH
+        POP: TOTPOP
+        ACRES: TOTACRE
+        emp_total: TOTEMP
+    annotate:
+      SPEC: annotate_landuse
+      DF: land_use
+  - tablename: accessibility
+    column_map:
+        column_1: nonmandatory_auto_accessibility
+        column_2: nonmandatory_transit_accessibility
+        column_3: nonmandatory_nm_accessibility
+        column_4: nonmandatory_sov0_accessibility
+        column_5: nonmandatory_sov1_accessibility
+        column_6: nonmandatory_sov2_accessibility
+        column_7: nonmandatory_hov0_accessibility
+        column_8: nonmandatory_hov1_accessibility
+        column_9: nonmandatory_hov2_accessibility
+        column_10: shop_hov_insufficient_accessibility
+        column_11: shop_hov_sufficient_accessibility
+        column_12: shop_hov_oversufficient_accessibility
+        column_13: maint_hov_insufficient_accessibility
+        column_14: maint_hov_sufficient_accessibility
+        column_15: maint_hov_oversufficient_accessibility
+        column_16: eat_hov_insufficient_accessibility
+        column_17: eat_hov_sufficient_accessibility
+        column_18: eat_hov_oversufficient_accessibility
+        column_19: visit_hov_insufficient_accessibility
+        column_20: visit_hov_sufficient_accessibility
+        column_21: visit_hov_oversufficient_accessibility
+        column_22: discr_hov_insufficient_accessibility
+        column_23: discr_hov_sufficient_accessibility
+        column_24: discr_hov_oversufficient_accessibility
+        column_25: escort_hov_insufficient_accessibility
+        column_26: escort_hov_sufficient_accessibility
+        column_27: escort_hov_oversufficient_accessibility
+        column_28: shop_sov_insufficient_accessibility
+        column_29: shop_sov_sufficient_accessibility
+        column_30: shop_sov_oversufficient_accessibility
+        column_31: maint_sov_insufficient_accessibility
+        column_32: maint_sov_sufficient_accessibility
+        column_33: maint_sov_oversufficient_accessibility
+        column_40: discr_sov_insufficient_accessibility
+        column_41: discr_sov_sufficient_accessibility
+        column_42: discr_sov_oversufficient_accessibility
+        column_45: total_emp_accessibility
+        column_47: hh_walktransit_accessibility
+        mgra : zone_id
\ No newline at end of file
diff --git a/test/auto_ownership/configs/network_los.yaml b/test/auto_ownership/configs/network_los.yaml
new file mode 100644
index 000000000..391125a38
--- /dev/null
+++ b/test/auto_ownership/configs/network_los.yaml
@@ -0,0 +1,14 @@
+# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
+read_skim_cache: False
+# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
+write_skim_cache: True
+
+zone_system: 1
+
+taz_skims: skims.omx
+
+skim_time_periods:
+    time_window: 1440
+    period_minutes: 60
+    periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59
+    labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV']
\ No newline at end of file
diff --git a/test/auto_ownership/configs/settings.yaml b/test/auto_ownership/configs/settings.yaml
new file mode 100644
index 000000000..ce94eef8e
--- /dev/null
+++ b/test/auto_ownership/configs/settings.yaml
@@ -0,0 +1,65 @@
+# input tables
+input_table_list:
+  - tablename: households
+    filename: households.csv
+    index_col: household_id
+    rename_columns:
+      unique_hh_id: household_id
+      NP: hhsize
+      hh_workers_from_esr: num_workers
+      VEH: auto_ownership
+      MAZ: home_zone_id
+      HINCP: income
+    keep_columns:
+      - home_zone_id
+      - income
+      - hhsize
+      - HHT
+      - auto_ownership
+      - num_workers
+  - tablename: persons
+    filename: persons.csv
+    index_col: person_id
+    rename_columns:
+      unique_hh_id: household_id
+      AGEP: age
+      SPORDER: PNUM
+      SEX: sex
+      employed: pemploy
+      student_status: pstudent
+      person_type: ptype
+    keep_columns:
+      - household_id
+      - age
+      - PNUM
+      - sex
+      - pemploy
+      - pstudent
+      - ptype
+  - tablename: land_use
+    filename: land_use.csv
+    index_col: zone_id
+    rename_columns:
+      MAZ_ORIGINAL: zone_id
+      CountyID: county_id
+      TAZ_ORIGINAL: TAZ
+      DistID: DISTRICT
+      HH: TOTHH
+      POP: TOTPOP
+      ACRES: TOTACRE
+      emp_total: TOTEMP
+    keep_columns:
+      - TAZ
+      - DISTRICT
+      - SD
+      - county_id
+      - TOTHH
+      - TOTPOP
+      - TOTACRE
+
+#trace_hh_id: 357022
+trace_hh_id: 36
+
+use_accessibility: 0
+
+mobility_av_share: 0.0
\ No newline at end of file
diff --git a/test/auto_ownership/configs/tour_departure_and_duration_alternatives.csv b/test/auto_ownership/configs/tour_departure_and_duration_alternatives.csv
new file mode 100644
index 000000000..bddab06b9
--- /dev/null
+++ b/test/auto_ownership/configs/tour_departure_and_duration_alternatives.csv
@@ -0,0 +1,191 @@
+start,end
+5,5
+5,6
+5,7
+5,8
+5,9
+5,10
+5,11
+5,12
+5,13
+5,14
+5,15
+5,16
+5,17
+5,18
+5,19
+5,20
+5,21
+5,22
+5,23
+6,6
+6,7
+6,8
+6,9
+6,10
+6,11
+6,12
+6,13
+6,14
+6,15
+6,16
+6,17
+6,18
+6,19
+6,20
+6,21
+6,22
+6,23
+7,7
+7,8
+7,9
+7,10
+7,11
+7,12
+7,13
+7,14
+7,15
+7,16
+7,17
+7,18
+7,19
+7,20
+7,21
+7,22
+7,23
+8,8
+8,9
+8,10
+8,11
+8,12
+8,13
+8,14
+8,15
+8,16
+8,17
+8,18
+8,19
+8,20
+8,21
+8,22
+8,23
+9,9
+9,10
+9,11
+9,12
+9,13
+9,14
+9,15
+9,16
+9,17
+9,18
+9,19
+9,20
+9,21
+9,22
+9,23
+10,10
+10,11
+10,12
+10,13
+10,14
+10,15
+10,16
+10,17
+10,18
+10,19
+10,20
+10,21
+10,22
+10,23
+11,11
+11,12
+11,13
+11,14
+11,15
+11,16
+11,17
+11,18
+11,19
+11,20
+11,21
+11,22
+11,23
+12,12
+12,13
+12,14
+12,15
+12,16
+12,17
+12,18
+12,19
+12,20
+12,21
+12,22
+12,23
+13,13
+13,14
+13,15
+13,16
+13,17
+13,18
+13,19
+13,20
+13,21
+13,22
+13,23
+14,14
+14,15
+14,16
+14,17
+14,18
+14,19
+14,20
+14,21
+14,22
+14,23
+15,15
+15,16
+15,17
+15,18
+15,19
+15,20
+15,21
+15,22
+15,23
+16,16
+16,17
+16,18
+16,19
+16,20
+16,21
+16,22
+16,23
+17,17
+17,18
+17,19
+17,20
+17,21
+17,22
+17,23
+18,18
+18,19
+18,20
+18,21
+18,22
+18,23
+19,19
+19,20
+19,21
+19,22
+19,23
+20,20
+20,21
+20,22
+20,23
+21,21
+21,22
+21,23
+22,22
+22,23
+23,23
\ No newline at end of file
diff --git a/test/auto_ownership/data/.gitkeep b/test/auto_ownership/data/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/auto_ownership/test_auto_ownership.py b/test/auto_ownership/test_auto_ownership.py
new file mode 100644
index 000000000..63a49969d
--- /dev/null
+++ b/test/auto_ownership/test_auto_ownership.py
@@ -0,0 +1,434 @@
+import logging
+import pytest
+import os
+import shutil
+import pandas as pd
+import numpy as np
+from numpy import dot
+from numpy.linalg import norm
+import orca
+from numpy.random import randint
+import scipy.stats as stats
+
+# import models is necessary to initalize the model steps with orca
+from activitysim.abm import models
+from activitysim.core import pipeline, config
+from activitysim.core import tracing
+
+logger = logging.getLogger(__name__)
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def module() -> str:
+    """
+    A pytest fixture that returns the data folder location.
+    :return: folder location for any necessary data to initialize the tests
+    """
+    return "auto_ownership"
+
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def tables(prepare_module_inputs) -> dict[str, str]:
+    """
+    A pytest fixture that returns the "mock" tables to build pipeline dataframes. The
+    key-value pair is the name of the table and the index column.
+    :return: dict
+    """
+    return {
+        "land_use": "MAZ_ORIGINAL",
+        "persons": "person_id",
+        "households": "household_id",
+        "accessibility": "MAZ_ORIGINAL",
+    }
+
+
+# Used by conftest.py initialize_pipeline method
+# Set to true if you need to read skims into the pipeline
+@pytest.fixture(scope="module")
+def initialize_network_los() -> bool:
+    """
+    A pytest boolean fixture indicating whether network skims should be read from the
+    fixtures test data folder.
+    :return: bool
+    """
+    return False
+
+
+# Used by conftest.py reconnect_pipeline method
+@pytest.fixture(scope="module")
+def load_checkpoint() -> bool:
+    """
+    checkpoint to be loaded from the pipeline when reconnecting.
+    """
+    return "initialize_households"
+
+
+@pytest.mark.skipif(
+    os.path.isfile("test/auto_ownership/output/pipeline.h5"),
+    reason="no need to recreate pipeline store if alreayd exist",
+)
+def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog):
+    # Run summarize model
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(models=["initialize_landuse", "initialize_households"])
+
+    pipeline.close_pipeline()
+
+
+def test_auto_ownership(reconnect_pipeline: pipeline.Pipeline, caplog):
+
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(
+        models=["auto_ownership_simulate"], resume_after="initialize_households"
+    )
+
+    # get the updated pipeline data
+    household_df = pipeline.get_table("households")
+    # logger.info("household_df columns: ", household_df.columns.tolist())
+
+    # target_col = "autos"
+    target_col = "pre_autos"
+    choice_col = "auto_ownership"
+    simulated_col = "autos_model"
+
+    similarity_threshold = 0.99
+
+    ao_alternatives_df = pd.DataFrame.from_dict(
+        {
+            choice_col: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "auto_choice_label": [
+                "0_CARS",
+                "1_CAR_1CV",
+                "1_CAR_1AV",
+                "2_CARS_2CV",
+                "2_CARS_2AV",
+                "2_CARS_1CV1AV",
+                "3_CARS_3CV",
+                "3_CARS_3AV",
+                "3_CARS_2CV1AV",
+                "3_CARS_1CV2AV",
+                "4_CARS_4CV",
+            ],
+            simulated_col: [0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4],
+        }
+    )
+
+    household_df = pd.merge(household_df, ao_alternatives_df, how="left", on=choice_col)
+
+    # AO summary from the model
+    simulated_df = create_summary(
+        household_df, key=simulated_col, out_col="Simulated_Share"
+    )
+
+    # AO summary from the results/target
+    target_df = create_summary(household_df, key=target_col, out_col="Target_Share")
+
+    merged_df = pd.merge(
+        target_df, simulated_df, left_on=target_col, right_on=simulated_col, how="outer"
+    )
+    merged_df = merged_df.fillna(0)
+
+    # compare simulated and target results by computing the cosine similarity between them
+    similarity_value = cosine_similarity(
+        merged_df["Target_Share"].tolist(), merged_df["Simulated_Share"].tolist()
+    )
+
+    # save the results to disk
+    merged_df.to_csv(
+        os.path.join("test", "auto_ownership", "output", "ao_test_results.csv"),
+        index=False,
+    )
+
+    # if the cosine_similarity >= threshold then the simulated and target results are "similar"
+    assert similarity_value >= similarity_threshold
+
+
+@pytest.mark.skip
+def test_auto_ownership_variation(reconnect_pipeline: pipeline.Pipeline, caplog):
+
+    caplog.set_level(logging.INFO)
+
+    output_file = os.path.join(
+        "test", "auto_ownership", "output", "ao_results_variation.csv"
+    )
+
+    if os.path.isfile(output_file):
+        out_df = pd.read_csv(output_file)
+
+        pipeline.open_pipeline(resume_after="initialize_households")
+
+        household_df = pipeline.get_table("households")
+
+    else:
+        target_col = "pre_autos"
+        choice_col = "auto_ownership"
+        simulated_col = "autos_model"
+
+        ao_alternatives_df = pd.DataFrame.from_dict(
+            {
+                choice_col: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+                "auto_choice_label": [
+                    "0_CARS",
+                    "1_CAR_1CV",
+                    "1_CAR_1AV",
+                    "2_CARS_2CV",
+                    "2_CARS_2AV",
+                    "2_CARS_1CV1AV",
+                    "3_CARS_3CV",
+                    "3_CARS_3AV",
+                    "3_CARS_2CV1AV",
+                    "3_CARS_1CV2AV",
+                    "4_CARS_4CV",
+                ],
+                simulated_col: [0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4],
+            }
+        )
+
+        NUM_SEEDS = 100
+
+        for i in range(1, NUM_SEEDS + 1):
+            base_seed = randint(1, 99999)
+            orca.add_injectable("rng_base_seed", base_seed)
+
+            # run model step
+            pipeline.run(
+                models=["auto_ownership_simulate"], resume_after="initialize_households"
+            )
+
+            # get the updated pipeline data
+            household_df = pipeline.get_table("households")
+
+            household_df = pd.merge(
+                household_df, ao_alternatives_df, how="left", on=choice_col
+            )
+
+            # AO summary from the model
+            simulated_share_col_name = "simulation_" + str(i)
+            simulated_df = create_summary(
+                household_df, key=simulated_col, out_col=simulated_share_col_name
+            )
+
+            if i == 1:
+                out_df = create_summary(household_df, key=target_col, out_col="target")
+
+            out_df = pd.concat(
+                [out_df, simulated_df[[simulated_share_col_name]]], axis=1
+            )
+
+            # since model_name is used as checkpoint name, the same model can not be run more than once.
+            # have to close the pipeline before running the same model again.
+            pipeline.close_pipeline()
+
+        out_df["simulation_min"] = out_df.filter(like="simulation_").min(axis=1)
+        out_df["simulation_max"] = out_df.filter(like="simulation_").max(axis=1)
+        out_df["simulation_mean"] = out_df.filter(like="simulation_").mean(axis=1)
+
+        # reorder columns
+        cols = [
+            "pre_autos",
+            "target",
+            "simulation_mean",
+            "simulation_min",
+            "simulation_max",
+        ]
+        cols = cols + [x for x in out_df.columns if x not in cols]
+        out_df = out_df[cols]
+
+        out_df.to_csv(output_file, index=False)
+
+    # chi-square test
+    alpha = 0.05
+    observed_prob = out_df["simulation_mean"]
+    expected_prob = out_df["target"]
+
+    num_hh = len(household_df)
+
+    observed = [p * num_hh for p in observed_prob]
+    expected = [p * num_hh for p in expected_prob]
+
+    observed_sum = sum(observed)
+    expected_sum = sum(expected)
+
+    # the sum of the observed and expected frequencies must be the same for the test
+    observed = [f * expected_sum / observed_sum for f in observed]
+
+    chi_square, prob = stats.chisquare(observed, expected)
+    p_value = 1 - prob
+
+    # Ho: Target and Simulated results are from same distribution and there is no significant difference b/w the two.
+    # Ha: Target and Simulated results are statistically different.
+
+    conclusion = "Failed to reject the null hypothesis."
+    if p_value >= alpha:
+        conclusion = "Null Hypothesis is rejected. Obersved and Simulated results are statistically different."
+
+    logger.info(conclusion)
+
+    # if p-value is less than alpha, then difference between simulated and target results
+    # are statistically greater than the random variation of the model results
+    assert p_value < alpha
+
+
+# fetch/prepare existing files for model inputs
+# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim
+@pytest.fixture(scope="module")
+def prepare_module_inputs() -> None:
+    """
+    copy input files from sharepoint into test folder
+
+    create unique person id in person file
+
+    :return: None
+    """
+    # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/
+    # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1
+    accessibility_file = os.path.join(
+        "test", "auto_ownership", "data", "accessibilities.csv"
+    )
+    household_file = os.path.join(
+        "test", "auto_ownership", "data", "popsyn", "households.csv"
+    )
+    person_file = os.path.join(
+        "test", "auto_ownership", "data", "popsyn", "persons.csv"
+    )
+    landuse_file = os.path.join(
+        "test", "auto_ownership", "data", "landuse", "maz_data_withDensity.csv"
+    )
+
+    test_dir = os.path.join("test", "auto_ownership", "data")
+
+    shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv"))
+    shutil.copy(household_file, os.path.join(test_dir, "households.csv"))
+    shutil.copy(person_file, os.path.join(test_dir, "persons.csv"))
+    shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv"))
+
+    # add original maz id to accessibility table
+    land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv"))
+
+    accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv"))
+
+    accessibility_df = pd.merge(
+        accessibility_df,
+        land_use_df[["MAZ", "MAZ_ORIGINAL"]].rename(columns={"MAZ": "mgra"}),
+        how="left",
+        on="mgra",
+    )
+
+    accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False)
+
+    # currently household file has to have these two columns, even before annotation
+    # because annotate person happens before household and uses these two columns
+    # TODO find a way to get around this
+    ####
+
+    # household file from populationsim
+    household_df = pd.read_csv(os.path.join(test_dir, "households.csv"))
+
+    household_columns_dict = {"HHID": "household_id", "MAZ": "home_zone_id"}
+
+    household_df.rename(columns=household_columns_dict, inplace=True)
+
+    # get columns from ctramp output
+    tm2_simulated_household_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "householdData_3.csv")
+    )
+    tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    household_df = pd.merge(
+        household_df,
+        tm2_simulated_household_df[
+            [
+                "household_id",
+                "autos",
+                "automated_vehicles",
+                "transponder",
+                "cdap_pattern",
+                "jtf_choice",
+            ]
+        ],
+        how="inner",  # tm2 is not 100% sample run
+        on="household_id",
+    )
+
+    tm2_pre_ao_results_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "aoResults_pre.csv")
+    )
+    tm2_pre_ao_results_df.rename(
+        columns={"HHID": "household_id", "AO": "pre_autos"}, inplace=True
+    )
+
+    household_df = pd.merge(
+        household_df, tm2_pre_ao_results_df, how="inner", on="household_id"
+    )
+
+    household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False)
+
+    # person file from populationsim
+    person_df = pd.read_csv(os.path.join(test_dir, "persons.csv"))
+
+    person_columns_dict = {"HHID": "household_id", "PERID": "person_id"}
+
+    person_df.rename(columns=person_columns_dict, inplace=True)
+
+    # get columns from ctramp result
+    tm2_simulated_person_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "personData_3.csv")
+    )
+    tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    person_df = pd.merge(
+        person_df,
+        tm2_simulated_person_df[
+            [
+                "household_id",
+                "person_id",
+                "type",
+                "value_of_time",
+                "activity_pattern",
+                "imf_choice",
+                "inmf_choice",
+                "fp_choice",
+                "reimb_pct",
+                "workDCLogsum",
+                "schoolDCLogsum",
+            ]
+        ],
+        how="inner",  # ctramp might not be 100% sample run
+        on=["household_id", "person_id"],
+    )
+
+    person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False)
+
+
+def create_summary(input_df, key, out_col="Share") -> pd.DataFrame:
+    """
+    Create summary for the input data.
+    1. group input data by the "key" column
+    2. calculate the percent of input data records in each "key" category.
+
+    :return: pd.DataFrame
+    """
+
+    out_df = input_df.groupby(key).size().reset_index(name="Count")
+    out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4)
+
+    return out_df[[key, out_col]]
+
+
+def cosine_similarity(a, b):
+    """
+    Computes cosine similarity between two vectors.
+
+    Cosine similarity is used here as a metric to measure similarity between two sequence of numbers.
+    Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them
+    i.e., dot products of the vectors divided by the product of their lengths.
+
+    :return:
+    """
+
+    return dot(a, b) / (norm(a) * norm(b))
diff --git a/test/cdap/.gitkeep b/test/cdap/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/cdap/configs/annotate_households.csv b/test/cdap/configs/annotate_households.csv
new file mode 100644
index 000000000..bd6dc6fef
--- /dev/null
+++ b/test/cdap/configs/annotate_households.csv
@@ -0,0 +1,18 @@
+Description,Target,Expression
+#,, annotate households table after import
+,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
+number of non_workers,num_non_workers,households.hhsize - households.num_workers
+number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)"
+num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)"
+num_children,num_children,"_PERSON_COUNT('~adult', persons, households)"
+num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)"
+num_children_6_to_15,num_children_6_to_15,"_PERSON_COUNT('6 <= age <= 15', persons, households)"
+num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)"
+num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)"
+# get definition from travel model two
+num_young_adults,num_young_adults,"_PERSON_COUNT('18 <= age <= 24', persons, households)"
+num_young_retirees,num_young_retirees,"_PERSON_COUNT('65 <= age <= 79', persons, households)"
+num_old_retirees,num_old_retirees,"_PERSON_COUNT('80 <= age', persons, households)"
+num_persons_18_to_35,num_persons_18_to_35,"_PERSON_COUNT('18 <= age <= 35', persons, households)"
+num_persons_65_plus,num_persons_65_plus,"_PERSON_COUNT('65 <= age', persons, households)"
+num_highschool_graduates,num_highschool_graduates,"_PERSON_COUNT('9 <= education_attainment', persons, households)"
\ No newline at end of file
diff --git a/test/cdap/configs/annotate_households_cdap.csv b/test/cdap/configs/annotate_households_cdap.csv
new file mode 100644
index 000000000..cd66b1c42
--- /dev/null
+++ b/test/cdap/configs/annotate_households_cdap.csv
@@ -0,0 +1,11 @@
+Description,Target,Expression
+#,, annotate households table after cdap model has run
+num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults
+num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers
+#participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0)
+participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1)
+cdap_activity,cdap_activity,"persons.cdap_activity.groupby(persons.household_id).apply(''.join).reindex(households.index).fillna('')"
\ No newline at end of file
diff --git a/test/cdap/configs/annotate_landuse.csv b/test/cdap/configs/annotate_landuse.csv
new file mode 100644
index 000000000..65237c0ad
--- /dev/null
+++ b/test/cdap/configs/annotate_landuse.csv
@@ -0,0 +1,6 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+household_density,household_density,land_use.TOTHH / land_use.TOTACRE
+employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE
+population_density,population_density,land_use.TOTPOP / land_use.TOTACRE
+density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
\ No newline at end of file
diff --git a/test/cdap/configs/annotate_persons.csv b/test/cdap/configs/annotate_persons.csv
new file mode 100644
index 000000000..0aea67814
--- /dev/null
+++ b/test/cdap/configs/annotate_persons.csv
@@ -0,0 +1,54 @@
+Description,Target,Expression
+#,, annotate persons table after import
+age_16_to_19,age_16_to_19,"persons.age.between(16, 19)"
+age_16_p,age_16_p,persons.age >= 16
+adult,adult,persons.age >= 18
+male,male,persons.sex == 1
+female,female,persons.sex == 2
+#,, annotate employment category
+#employment category - under employment age,empcat,
+#employment category - over employment age and not employed,empcat,
+#employment category - over employment age and working at least 35 hours a week and 27+ weeks a year,empcat,
+#employment category - over employment age and not working at least 35 hours a week and 27+ weeks a year,empcat,
+#,,TODO add ptype annotation
+,ptype,0
+,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)"
+,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)"
+,ptype,"np.where(persons.type == 'University student', 3, ptype)"
+,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)"
+,ptype,"np.where(persons.type == 'Retired', 5, ptype)"
+,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)"
+,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)"
+,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)"
+#full time worker,ptype
+#presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)"
+#presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)"
+#presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)"
+#presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)"
+#presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)"
+#presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)"
+#presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)"
+#presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)"
+#student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))"
+#nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))"
+#,,
+#,, FIXME - if person is a university student but has school age student category value then reset student category value
+#,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)"
+#,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student
+#,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)"
+#,, FIXME if student category is non-student and employment is student then reset student category value to student
+#,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)"
+#,,
+#is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])"
+#preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)"
+#preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)"
+#is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE)
+#is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE)
+#is_university,is_university,pstudent == PSTUDENT_UNIVERSITY
+#school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)"
+#school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)"
+#school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)"
+#,,
+#is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])"
+#,,
+home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)"
diff --git a/test/cdap/configs/annotate_persons_after_hh.csv b/test/cdap/configs/annotate_persons_after_hh.csv
new file mode 100644
index 000000000..0dfa16be6
--- /dev/null
+++ b/test/cdap/configs/annotate_persons_after_hh.csv
@@ -0,0 +1,5 @@
+Description,Target,Expression
+#,, annotate persons table after annotate_households
+#,, adults get full hh_value_of_time and children get 60%
+,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)"
+,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)"
\ No newline at end of file
diff --git a/test/cdap/configs/annotate_persons_cdap.csv b/test/cdap/configs/annotate_persons_cdap.csv
new file mode 100644
index 000000000..2ad5e56a6
--- /dev/null
+++ b/test/cdap/configs/annotate_persons_cdap.csv
@@ -0,0 +1,6 @@
+Description,Target,Expression
+#,, annotate persons table after cdap model has run
+travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME
+under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])"
+has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))"
+has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))"
diff --git a/test/cdap/configs/cdap.yaml b/test/cdap/configs/cdap.yaml
new file mode 100644
index 000000000..5758444c2
--- /dev/null
+++ b/test/cdap/configs/cdap.yaml
@@ -0,0 +1,49 @@
+
+INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv
+COEFFICIENTS: cdap_coefficients.csv
+INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv
+
+FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv
+
+# set to True if want to introduce joint tour utility in cdap
+# otherwise set to False or comment out, defaulted to False
+ADD_JOINT_TOUR_UTILITY: True
+JOINT_TOUR_COEFFICIENTS: cdap_joint_tour_coefficients.csv
+JOINT_TOUR_USEFUL_COLUMNS:
+  - autos
+  - income
+  - num_workers
+  - nonmandatory_hov0_accessibility
+  - nonmandatory_hov1_accessibility
+  - nonmandatory_hov2_accessibility
+  - WorkLocationLogsum
+
+CONSTANTS:
+  FULL: 1
+  PART: 2
+  UNIVERSITY: 3
+  NONWORK: 4
+  RETIRED: 5
+  DRIVING: 6
+  SCHOOL: 7
+  PRESCHOOL: 8
+
+PERSON_TYPE_MAP:
+  WORKER:
+    - 1
+    - 2
+  CHILD:
+    - 6
+    - 7
+    - 8
+
+annotate_persons:
+  SPEC: annotate_persons_cdap
+  DF: persons
+
+
+annotate_households:
+  SPEC: annotate_households_cdap
+  DF: households
+  TABLES:
+    - persons
diff --git a/test/cdap/configs/cdap_coefficients.csv b/test/cdap/configs/cdap_coefficients.csv
new file mode 100644
index 000000000..110ee2a44
--- /dev/null
+++ b/test/cdap/configs/cdap_coefficients.csv
@@ -0,0 +1,235 @@
+coefficient_name,value,constrain
+coef_UNAVAILABLE,-999,T
+coef_full_time_worker_asc_M,2.9114,F
+coef_full_time_worker_asc_N,-0.7695,F
+coef_part_time_worker_asc_M,2.9274,F
+coef_part_time_worker_asc_N,1.3675,F
+coef_university_student_asc_M,1.7642,F
+coef_university_student_asc_N,-0.3138,F
+coef_non_working_adult_asc_M,-3.1521,F
+coef_non_working_adult_asc_N,0.513,F
+coef_retired_asc_M,-2.7055,F
+coef_retired_asc_N,0.9234,F
+coef_driving_age_child_who_is_in_school_asc_M,3.2036,F
+coef_driving_age_child_who_is_in_school_asc_N,-3.4315,F
+coef_pre_driving_age_child_who_is_in_school_asc_M,7.0644,F
+coef_pre_driving_age_child_who_is_in_school_asc_N,2.574,F
+coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,1.1,F
+coef_pre_driving_age_child_who_is_too_young_for_school_asc_N,0.6017,F
+#,,
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,-1.5151,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_N,0.3702,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,3.2965,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_N,1.1392,F
+coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,-0.8582,F
+coef_full_time_worker_interaction_with_age_less_than_35_N,-0.145,F
+coef_part_time_worker_interaction_with_age_less_than_35_M,-0.7095,F
+coef_part_time_worker_interaction_with_age_less_than_35_N,-1.4213,F
+#,,
+coef_full_time_worker_interaction_with_female_gender_M,0.3032,F
+coef_full_time_worker_interaction_with_female_gender_N,0.7718,F
+coef_part_time_worker_interaction_with_female_gender_M,0.061,F
+coef_part_time_worker_interaction_with_female_gender_N,0.4176,F
+coef_university_student_interaction_with_female_gender_M,1.2429,F
+coef_university_student_interaction_with_female_gender_N,2.2549,F
+coef_non_working_adult_interaction_with_female_gender_N,0.1475,F
+coef_retired_interaction_with_female_M,-0.7751,F
+coef_retired_interaction_with_female_N,-0.3729,F
+coef_driving_age_child_who_is_in_school_interaction_with_female_N,0.7991,F
+#,,
+coef_full_time_worker_interaction_with_more_cars_than_workers_N,-0.087,F
+coef_non_working_adult_interaction_with_more_cars_than_workers_N,0.2122,F
+coef_retired_interaction_with_more_cars_than_workers_N,0.8642,F
+coef_driving_age_child_who_is_in_school_interaction_with_more_cars_than_workers_M,0.0988,F
+coef_full_time_worker_interaction_with_zero_cars_M,-0.3377,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_zero_cars_M,-0.5917,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_zero_cars_N,-1.4389,F
+coef_full_time_worker_interaction_with_fewer_cars_than_workers_M,-0.3377,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_M,-0.4778,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_N,-0.5259,F
+#,,
+coef_full_time_worker_interaction_with_income_less_than_30k_M,-0.7201,F
+coef_full_time_worker_interaction_with_income_less_than_30k_H,-0.5331,F
+coef_part_time_worker_interaction_with_income_less_than_30k_M,0.1285,F
+coef_university_student_interaction_with_income_less_than_30k_M,0.4359,F
+coef_university_student_interaction_with_income_between_60k_and_100k_H,0.6352,F
+coef_university_student_interaction_with_income_more_than_100k_H,0.6352,F
+coef_non_working_adult_interaction_with_income_more_than_100k_H,-0.2468,F
+coef_retired_interaction_with_income_more_than_100k_M,-0.1418,F
+coef_retired_interaction_with_income_more_than_100k_H,-0.2388,F
+coef_pre_driving_age_child_who_is_in_school_interaction_with_income_less_than_30k_M,1.2007,F
+coef_pre_driving_age_child_who_is_in_school_interaction_with_income_less_than_30k_H,1.9783,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_60k_and_100k_M,0.2952,F
+coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_M,0.2952,F
+#,,
+coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M,0.1212,F
+coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M,0.2004,F
+coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M,0.2314,F
+coef_retired_interaction_with_peak_accessibility_to_all_employment_M,0.2792,F
+coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N,0.0721,F
+coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N,0.0823,F
+#,,
+coef_university_student_interaction_with_school_location_mc_logsum_M,0.0243,F
+coef_full_time_worker_interaction_with_usual_work_location_is_home_M,-2.4147,F
+coef_part_time_worker_interaction_with_usual_work_location_is_home_M,-2.8801,F
+coef_full_time_worker_interaction_with_no_usual_work_location_M,-0.3777,F
+coef_part_time_worker_interaction_with_no_usual_work_location_M,-0.6869,F
+coef_full_time_worker_asc_adjustment_M,-0.2301,F
+coef_part_time_worker_asc_adjustment_M,-0.471,F
+coef_university_student_asc_adjustment_M,-0.3361,F
+coef_driving_age_child_who_is_in_school_asc_adjustment_M,0.1434,F
+coef_pre_driving_age_child_who_is_in_school_asc_adjustment_M,0.4725,F
+coef_pre_driving_age_child_who_is_too_young_for_school_asc_adjustment_M,0.5554,F
+coef_part_time_worker_asc_adjustment_tm2_calib_M,-0.3499,F
+coef_university_student_asc_adjustment_tm2_calib_M,-0.9471,F
+coef_non_working_adult_asc_adjustment_tm2_calib_M,0,F
+coef_retired_asc_adjustment_tm2_calib_M,0,F
+coef_full_time_worker_interaction_with_retail_accessibility_N,0.0445,F
+coef_non_working_adult_interaction_with_retail_accessibility_N,0.0069,F
+coef_driving_age_child_who_is_in_school_interaction_with_retail_accessibility_N,0.157,F
+coef_full_time_worker_interaction_with_usual_work_location_is_home_N,0.87616,F
+coef_full_time_worker_asc_adjustment_N,-0.1846,F
+coef_part_time_worker_asc_adjustment_N,-0.4229,F
+coef_university_student_asc_adjustment_N,0.0325,F
+coef_non_working_adult_asc_adjustment_N,0.077,F
+coef_retired_asc_adjustment_N,-0.0012,F
+coef_driving_age_child_who_is_in_school_asc_adjustment_N,-0.8075,F
+coef_pre_driving_age_child_who_is_in_school_asc_adjustment_N,-0.1462,F
+coef_pre_driving_age_child_who_is_too_young_for_school_asc_adjustment_N,0.134,F
+coef_part_time_worker_asc_adjustment_tm2_calib_N,0.5217,F
+coef_university_student_asc_adjustment_tm2_calib_N,-0.6678,F
+coef_non_working_adult_asc_adjustment_tm2_calib_N,0.0466,F
+coef_retired_asc_adjustment_tm2_calib_N,0.1313,F
+coef_full_time_worker_interaction_with_detached_home_H,0.1538,F
+coef_part_time_worker_interaction_with_detached_home_H,0.0862,F
+coef_retired_interaction_with_detached_home_H,0.7415,F
+coef_driving_age_child_who_is_in_school_interaction_with_detached_home_H,2.023,F
+#,,
+# 2-way interactions,,
+coef_H_11,0.7511,F
+coef_H_12,0,F
+coef_H_13,0,F
+coef_H_14,0,F
+coef_H_15,0.6692,F
+coef_H_16,1.3472,F
+coef_H_17,0,F
+coef_H_18,0.7797,F
+coef_H_22,0.7897,F
+coef_H_23,1.617,F
+coef_H_24,1.1606,F
+coef_H_25,0.7915,F
+coef_H_26,0,F
+coef_H_27,1.8203,F
+coef_H_28,1.7547,F
+coef_H_33,0,F
+coef_H_34,0.637,F
+coef_H_35,0.1955,F
+coef_H_36,2.2375,F
+coef_H_37,0,F
+coef_H_38,1.7118,F
+coef_H_44,1.2214,F
+coef_H_45,0.8544,F
+coef_H_46,1.116,F
+coef_H_47,1.974,F
+coef_H_48,2.1615,F
+coef_H_55,1.0484,F
+coef_H_56_57,0,F
+coef_H_58,1.9117,F
+coef_H_66,3.192,F
+coef_H_67,0,F
+coef_H_68,0,F
+coef_H_77,5.6222,F
+coef_H_78,2.8078,F
+coef_H_88,3.2327,F
+coef_M_11,0,F
+coef_M_12,0,F
+coef_M_13,0.0627,F
+coef_M_16,0,F
+coef_M_17,0.1434,F
+coef_M_18,0.3851,F
+coef_M_22,0,F
+coef_M_23,0.5967,F
+coef_M_26,0,F
+coef_M_27,0.4024,F
+coef_M_28,0.4453,F
+coef_M_33,0.3881,F
+coef_M_36,0,F
+coef_M_37,0.2755,F
+coef_M_38,0.4148,F
+coef_M_66,0.6854,F
+coef_M_67,0.3692,F
+coef_M_68,0.5467,F
+coef_M_77,0.7729,F
+coef_M_78,0,F
+coef_M_88,0,F
+coef_N_11,0.15,F
+coef_N_12,0,F
+coef_N_13,0,F
+coef_N_14,0,F
+coef_N_15,0,F
+coef_N_16,1.0053,F
+coef_N_17,0.3041,F
+coef_N_18,0,F
+coef_N_22,0,F
+coef_N_23,0,F
+coef_N_24,0,F
+coef_N_25,0,F
+coef_N_26,0,F
+coef_N_27,0.3248,F
+coef_N_28,0.9231,F
+coef_N_33,0,F
+coef_N_34,0,F
+coef_N_35,0.8526,F
+coef_N_36,0.9678,F
+coef_N_37,0,F
+coef_N_38,0.9241,F
+coef_N_44,0,F
+coef_N_45,0,F
+coef_N_46,0.7134,F
+coef_N_47,0.8509,F
+coef_N_48,1.1721,F
+coef_N_55,0,F
+coef_N_56_57_58,0,F
+coef_N_66,0,F
+coef_N_67,1.8265,F
+coef_N_68,1.1744,F
+coef_N_77,2.5719,F
+coef_N_78,0.7036,F
+coef_N_88,0.4338,F
+# 3-way interactions,,
+coef_H_114,1.5072,F
+coef_H_117_118,0.8382,F
+coef_H_127_128,0.4246,F
+coef_H_147_148,0.1548,F
+coef_H_177_178_187_188,-0.7547,F
+coef_H_277_278_287_288,-2.2535,F
+coef_H_447_448,-0.9024,F
+coef_H_477_478_487_488,-1.3723,F
+coef_H_777_778_788_888,-2.4703,F
+coef_M_111,0.298,F
+coef_M_112,0.2032,F
+coef_M_122,-0.6279,F
+coef_M_127_128,-0.0432,F
+coef_M_177_178_187_188,-0.1301,F
+coef_M_227_228,-0.1524,F
+coef_M_277_278_287_288,-0.1259,F
+coef_M_777_778_788_888,-0.0112,F
+coef_N_117_118,-0.5454,F
+coef_N_144,-0.9496,F
+coef_N_127_128,-1.7459,F
+coef_N_147_148,-0.1659,F
+coef_N_177_178_187_188,0.4687,F
+coef_N_222,1.8781,F
+coef_N_277_278_287_288,-0.6913,F
+coef_N_477_478_487_488,-0.4894,F
+coef_N_777_778_788_888,-0.0582,F
+# cdap_all_people,,
+coef_M_xxx,-0.114,F
+coef_N_xxx,-0.4673,F
+coef_H_xxx,-0.1538,F
+coef_M_xxxx,0.4569,F
+coef_N_xxxx,-0.4669,F
+coef_H_xxxx,-0.4645,F
+coef_M_xxxxx,-0.2607,F
+coef_N_xxxxx,-1.4859,F
+coef_H_xxxxx,-9,F
diff --git a/test/cdap/configs/cdap_fixed_relative_proportions.csv b/test/cdap/configs/cdap_fixed_relative_proportions.csv
new file mode 100644
index 000000000..788f398b6
--- /dev/null
+++ b/test/cdap/configs/cdap_fixed_relative_proportions.csv
@@ -0,0 +1,9 @@
+Description,Expression,M,N,H
+Full-time worker,ptype == 1,0.79647,0.09368,0.10985
+Part-time worker,ptype == 2,0.61678,0.25757,0.12565
+University student,ptype == 3,0.69229,0.15641,0.1513
+Non-working adult,ptype == 4,0,0.67169,0.32831
+Retired,ptype == 5,0,0.54295,0.45705
+Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387
+Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342
+Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432
diff --git a/test/cdap/configs/cdap_indiv_and_hhsize1.csv b/test/cdap/configs/cdap_indiv_and_hhsize1.csv
new file mode 100644
index 000000000..a5cb0d660
--- /dev/null
+++ b/test/cdap/configs/cdap_indiv_and_hhsize1.csv
@@ -0,0 +1,81 @@
+Description,Expression,M,N,H
+Full-time worker alternative-specific constants,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N,
+Part-time worker alternative-specific constants,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N,
+University student alternative-specific constants,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N,
+Non-working adult alternative-specific constants,ptype == 4,coef_non_working_adult_asc_M,coef_non_working_adult_asc_N,
+Retired alternative-specific constants,ptype == 5,coef_retired_asc_M,coef_retired_asc_N,
+Driving-age child who is in school alternative-specific constants,ptype == 6,coef_driving_age_child_who_is_in_school_asc_M,coef_driving_age_child_who_is_in_school_asc_N,
+Pre-driving-age child who is in school alternative-specific constants,ptype == 7,coef_pre_driving_age_child_who_is_in_school_asc_M,coef_pre_driving_age_child_who_is_in_school_asc_N,
+Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,coef_pre_driving_age_child_who_is_too_young_for_school_asc_N,
+#,,,,
+Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_N,
+Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_N,
+Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,,
+Full-time worker interaction with age less than 35,(ptype == 1) & (age < 35),,coef_full_time_worker_interaction_with_age_less_than_35_N,
+Part-time worker interaction with age less than 35,(ptype == 2) & (age < 35),coef_part_time_worker_interaction_with_age_less_than_35_N,coef_part_time_worker_interaction_with_age_less_than_35_N,
+Retired interaction with age more than 80,(ptype == 5) & (age > 80),,,
+#,,,,
+Full-time worker interaction with female gender,(ptype == 1) & (sex == 2),coef_full_time_worker_interaction_with_female_gender_M,coef_full_time_worker_interaction_with_female_gender_N,
+Part-time worker interaction with female gender,(ptype == 2) & (sex == 2),coef_part_time_worker_interaction_with_female_gender_M,coef_part_time_worker_interaction_with_female_gender_N,
+University student interaction with female gender,(ptype == 3) & (sex == 2),coef_university_student_interaction_with_female_gender_M,coef_university_student_interaction_with_female_gender_N,
+Non-working adult interaction with female gender,(ptype == 4) & (sex == 2),,coef_non_working_adult_interaction_with_female_gender_N,
+Retired interaction with female,(ptype == 5) & (sex == 2),coef_retired_interaction_with_female_M,coef_retired_interaction_with_female_N,
+Driving-age child interaction with female,(ptype == 6) & (sex == 2),,coef_driving_age_child_who_is_in_school_interaction_with_female_N,
+#,,,,
+Full-time worker interaction with more cars than workers,(ptype == 1) & (autos > num_workers),,coef_full_time_worker_interaction_with_more_cars_than_workers_N,
+Non-working adult interaction with more cars than workers,(ptype == 4) & (autos > num_workers),,coef_non_working_adult_interaction_with_more_cars_than_workers_N,
+Retired interaction with more cars than workers,(ptype == 5) & (autos > num_workers),,coef_retired_interaction_with_more_cars_than_workers_N,
+Driving-age child who is in school interaction with more cars than workers,(ptype == 6) & (autos > num_workers),coef_driving_age_child_who_is_in_school_interaction_with_more_cars_than_workers_M,,
+Full-time worker interaction with zero cars,(ptype == 1) & (autos==0),coef_full_time_worker_interaction_with_zero_cars_M,,
+Pre-driving-age child who is too young for school interaction with zero cars,(ptype == 8) & (autos==0),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_zero_cars_M,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_zero_cars_N,
+Full-time worker interaction with fewer cars than workers,(ptype == 1) & (autos < num_workers),coef_full_time_worker_interaction_with_fewer_cars_than_workers_M,,
+Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (autos < num_workers),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_M,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_N,
+#,,,,
+Full-time worker interaction with income less than $30k,(ptype == 1) & (income < 30000),coef_full_time_worker_interaction_with_income_less_than_30k_M,,coef_full_time_worker_interaction_with_income_less_than_30k_H
+Part-time worker interaction with income less than $30k,(ptype == 2) & (income < 30000),coef_part_time_worker_interaction_with_income_less_than_30k_M,,
+University student interaction with income less than $30k,(ptype == 3) & (income < 30000),coef_university_student_interaction_with_income_less_than_30k_M,,
+University student interaction with income between $60k and $100k,(ptype == 3) & (income >= 60000) & (income <= 100000),,,coef_university_student_interaction_with_income_between_60k_and_100k_H
+University student interaction with income more than $100k,(ptype == 3) & (income >= 100000),,,coef_university_student_interaction_with_income_more_than_100k_H
+Non-working adult interaction with income more than $100k,(ptype == 4) & (income >= 100000),,,coef_non_working_adult_interaction_with_income_more_than_100k_H
+Retired interaction with income between $60k and $100k,(ptype == 5) & (income >= 60000) & (income <= 100000),,,
+Retired interaction with income more than $100k,(ptype == 5) & (income >= 100000),coef_retired_interaction_with_income_more_than_100k_M,,coef_retired_interaction_with_income_more_than_100k_H
+Pre-driving-age child who is in school interaction with income less than $30k,(ptype == 7) & (income < 30000),coef_pre_driving_age_child_who_is_in_school_interaction_with_income_less_than_30k_M,,coef_pre_driving_age_child_who_is_in_school_interaction_with_income_less_than_30k_H
+Pre-driving-age child who is too young for school interaction with income between $60k and $100k,(ptype == 8) & (income >= 60000) & (income <= 100000),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_60k_and_100k_M,,
+Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income >= 100000),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_M,,
+#,,,,
+Uinversity Student interaction with peak accessibility to all employment,(ptype == 3) * SchoolLocationLogsum,coef_university_student_interaction_with_school_location_mc_logsum_M,,
+Full-time Worker interaction with accessibility to retail,"@np.where((df.ptype == 1)*(df.autos<df.num_workers),df.nonmandatory_hov0_accessibility,0)",,coef_full_time_worker_interaction_with_retail_accessibility_N,
+Full-time Worker interaction with accessibility to retail,"@np.where((df.ptype == 1)*(df.autos==df.num_workers),df.nonmandatory_hov1_accessibility,0)",,coef_full_time_worker_interaction_with_retail_accessibility_N,
+Full-time Worker interaction with accessibility to retail,"@np.where((df.ptype == 1)*(df.autos>df.num_workers),df.nonmandatory_hov2_accessibility,0)",,coef_full_time_worker_interaction_with_retail_accessibility_N,
+Non-working adult interaction with accessibility to retail,"@np.where((df.ptype == 4)*(df.autos<df.num_workers),df.nonmandatory_hov0_accessibility,0)",,coef_non_working_adult_interaction_with_retail_accessibility_N,
+Non-working adult interaction with accessibility to retail,"@np.where((df.ptype == 4)*(df.autos==df.num_workers),df.nonmandatory_hov1_accessibility,0)",,coef_non_working_adult_interaction_with_retail_accessibility_N,
+Non-working adult interaction with accessibility to retail,"@np.where((df.ptype == 4)*(df.autos>df.num_workers),df.nonmandatory_hov2_accessibility,0)",,coef_non_working_adult_interaction_with_retail_accessibility_N,
+Driving-age child who is in school interaction with accessibility to retail,"@np.where((df.ptype == 6)*(df.autos<df.num_workers),df.nonmandatory_hov0_accessibility,0)",,coef_driving_age_child_who_is_in_school_interaction_with_retail_accessibility_N,
+Driving-age child who is in school interaction with accessibility to retail,"@np.where((df.ptype == 6)*(df.autos==df.num_workers),df.nonmandatory_hov1_accessibility,0)",,coef_driving_age_child_who_is_in_school_interaction_with_retail_accessibility_N,
+Driving-age child who is in school interaction with accessibility to retail,"@np.where((df.ptype == 6)*(df.autos>df.num_workers),df.nonmandatory_hov2_accessibility,0)",,coef_driving_age_child_who_is_in_school_interaction_with_retail_accessibility_N,
+#,,,,
+Full-time worker interaction with usual work location is home,(ptype==1)&(WorkLocation==99999),coef_full_time_worker_interaction_with_usual_work_location_is_home_M,coef_full_time_worker_interaction_with_usual_work_location_is_home_N,
+Part-time worker interaction with usual work location is home,(ptype==2)&(WorkLocation==99999),coef_part_time_worker_interaction_with_usual_work_location_is_home_M,,
+Full-time worker interaction with no usual work location,(ptype==1)&((WorkLocation==99999)|(WorkLocation<=0)),coef_full_time_worker_interaction_with_no_usual_work_location_M,,
+Part-time worker interaction with no usual work location,(ptype==2)&((WorkLocation==99999)|(WorkLocation<=0)),coef_part_time_worker_interaction_with_no_usual_work_location_M,,
+#,,,,
+Full-time worker interaction with detached house,(ptype==1)&(building_size<3),,,coef_full_time_worker_interaction_with_detached_home_H
+Part-time worker interaction with detached house,(ptype==2)&(building_size<3),,,coef_part_time_worker_interaction_with_detached_home_H
+Retired interaction with detached house,(ptype==5)&(building_size<3),,,coef_retired_interaction_with_detached_home_H
+Pre-driving-age child in School interaction with detached house,(ptype==6)&(building_size<3),,,coef_driving_age_child_who_is_in_school_interaction_with_detached_home_H
+#,,,,
+Mandatory pattern unavailable for retired person,ptype==5,coef_UNAVAILABLE,,
+Mandatory pattern unavailable for nonworking adult person type,ptype==4,coef_UNAVAILABLE,,
+#,,,,
+Alternative Specific Constant Adjustment for Full-time worker,ptype == 1,coef_full_time_worker_asc_adjustment_M,coef_full_time_worker_asc_adjustment_N,
+Alternative Specific Constant Adjustment for Part-time worker,ptype == 2,coef_part_time_worker_asc_adjustment_M,coef_part_time_worker_asc_adjustment_N,
+Alternative Specific Constant Adjustment for University student,ptype == 3,coef_university_student_asc_adjustment_M,coef_university_student_asc_adjustment_N,
+Alternative Specific Constant Adjustment for Non-working adult,ptype == 4,,coef_non_working_adult_asc_adjustment_N,
+Alternative Specific Constant Adjustment for Retired,ptype == 5,,coef_retired_asc_adjustment_N,
+Alternative Specific Constant Adjustment for Driving-age child who is in school,ptype == 6,coef_driving_age_child_who_is_in_school_asc_adjustment_M,coef_driving_age_child_who_is_in_school_asc_adjustment_N,
+Alternative Specific Constant Adjustment for Pre-driving-age child who is in school,ptype == 7,coef_pre_driving_age_child_who_is_in_school_asc_adjustment_M,coef_pre_driving_age_child_who_is_in_school_asc_adjustment_N,
+Alternative Specific Constant Adjustment for Pre-driving-age child who is too young for school,ptype == 8,coef_pre_driving_age_child_who_is_too_young_for_school_asc_adjustment_M,coef_pre_driving_age_child_who_is_too_young_for_school_asc_adjustment_N,
+ASC Adjustment for Part-time worker -- TM2 calibration,ptype == 2,coef_part_time_worker_asc_adjustment_tm2_calib_M,coef_part_time_worker_asc_adjustment_tm2_calib_N,
+ASC Adjustment for University student -- TM2 calibration,ptype == 3,coef_university_student_asc_adjustment_tm2_calib_M,coef_university_student_asc_adjustment_tm2_calib_N,
+ASC Adjustment for Non-working adult -- TM2 calibration,ptype == 4,coef_non_working_adult_asc_adjustment_tm2_calib_M,coef_non_working_adult_asc_adjustment_tm2_calib_N,
+ASC Adjustment for Retired --TM2 calibration,ptype == 5,coef_retired_asc_adjustment_tm2_calib_M,coef_retired_asc_adjustment_tm2_calib_N,
diff --git a/test/cdap/configs/cdap_interaction_coefficients.csv b/test/cdap/configs/cdap_interaction_coefficients.csv
new file mode 100644
index 000000000..f94fa1723
--- /dev/null
+++ b/test/cdap/configs/cdap_interaction_coefficients.csv
@@ -0,0 +1,177 @@
+activity,interaction_ptypes,coefficient
+# 2-way interactions,,
+H,11,coef_H_11
+H,12,coef_H_12
+H,13,coef_H_13
+H,14,coef_H_14
+H,15,coef_H_15
+H,16,coef_H_16
+H,17,coef_H_17
+H,18,coef_H_18
+H,22,coef_H_22
+H,23,coef_H_23
+H,24,coef_H_24
+H,25,coef_H_25
+H,26,coef_H_26
+H,27,coef_H_27
+H,28,coef_H_28
+H,33,coef_H_33
+H,34,coef_H_34
+H,35,coef_H_35
+H,36,coef_H_36
+H,37,coef_H_37
+H,38,coef_H_38
+H,44,coef_H_44
+H,45,coef_H_45
+H,46,coef_H_46
+H,47,coef_H_47
+H,48,coef_H_48
+H,55,coef_H_55
+H,56,coef_H_56_57
+H,57,coef_H_56_57
+H,58,coef_H_58
+H,66,coef_H_66
+H,67,coef_H_67
+H,68,coef_H_68
+H,77,coef_H_77
+H,78,coef_H_78
+H,88,coef_H_88
+M,11,coef_M_11
+M,12,coef_M_12
+M,13,coef_M_13
+M,16,coef_M_16
+M,17,coef_M_17
+M,18,coef_M_18
+M,22,coef_M_22
+M,23,coef_M_23
+M,26,coef_M_26
+M,27,coef_M_27
+M,28,coef_M_28
+M,33,coef_M_33
+M,36,coef_M_36
+M,37,coef_M_37
+M,38,coef_M_38
+M,66,coef_M_66
+M,67,coef_M_67
+M,68,coef_M_68
+M,77,coef_M_77
+M,78,coef_M_78
+M,88,coef_M_88
+N,11,coef_N_11
+N,12,coef_N_12
+N,13,coef_N_13
+N,14,coef_N_14
+N,15,coef_N_15
+N,16,coef_N_16
+N,17,coef_N_17
+N,18,coef_N_18
+N,22,coef_N_22
+N,23,coef_N_23
+N,24,coef_N_24
+N,25,coef_N_25
+N,26,coef_N_26
+N,27,coef_N_27
+N,28,coef_N_28
+N,33,coef_N_33
+N,34,coef_N_34
+N,35,coef_N_35
+N,36,coef_N_36
+N,37,coef_N_37
+N,38,coef_N_38
+N,44,coef_N_44
+N,45,coef_N_45
+N,46,coef_N_46
+N,47,coef_N_47
+N,48,coef_N_48
+N,55,coef_N_55
+N,56,coef_N_56_57_58
+N,57,coef_N_56_57_58
+N,58,coef_N_56_57_58
+N,66,coef_N_66
+N,67,coef_N_67
+N,68,coef_N_68
+N,77,coef_N_77
+N,78,coef_N_78
+N,88,coef_N_88
+# 3-way interactions,,
+H,114,coef_H_114
+H,117,coef_H_117_118
+H,118,coef_H_117_118
+H,127,coef_H_127_128
+H,128,coef_H_127_128
+H,147,coef_H_147_148
+H,148,coef_H_147_148
+H,177,coef_H_177_178_187_188
+H,178,coef_H_177_178_187_188
+H,187,coef_H_177_178_187_188
+H,188,coef_H_177_178_187_188
+H,277,coef_H_277_278_287_288
+H,278,coef_H_277_278_287_288
+H,287,coef_H_277_278_287_288
+H,288,coef_H_277_278_287_288
+H,447,coef_H_447_448
+H,448,coef_H_447_448
+H,477,coef_H_477_478_487_488
+H,478,coef_H_477_478_487_488
+H,487,coef_H_477_478_487_488
+H,488,coef_H_477_478_487_488
+H,777,coef_H_777_778_788_888
+H,778,coef_H_777_778_788_888
+H,788,coef_H_777_778_788_888
+H,888,coef_H_777_778_788_888
+M,111,coef_M_111
+M,112,coef_M_112
+M,122,coef_M_122
+M,127,coef_M_127_128
+M,128,coef_M_127_128
+M,177,coef_M_177_178_187_188
+M,178,coef_M_177_178_187_188
+M,187,coef_M_177_178_187_188
+M,188,coef_M_177_178_187_188
+M,227,coef_M_227_228
+M,228,coef_M_227_228
+M,277,coef_M_277_278_287_288
+M,278,coef_M_277_278_287_288
+M,287,coef_M_277_278_287_288
+M,288,coef_M_277_278_287_288
+M,777,coef_M_777_778_788_888
+M,778,coef_M_777_778_788_888
+M,788,coef_M_777_778_788_888
+M,888,coef_M_777_778_788_888
+N,117,coef_N_117_118
+N,118,coef_N_117_118
+N,144,coef_N_144
+N,127,coef_N_127_128
+N,128,coef_N_127_128
+N,147,coef_N_147_148
+N,148,coef_N_147_148
+N,177,coef_N_177_178_187_188
+N,178,coef_N_177_178_187_188
+N,187,coef_N_177_178_187_188
+N,188,coef_N_177_178_187_188
+N,222,coef_N_222
+N,277,coef_N_277_278_287_288
+N,278,coef_N_277_278_287_288
+N,287,coef_N_277_278_287_288
+N,288,coef_N_277_278_287_288
+N,477,coef_N_477_478_487_488
+N,478,coef_N_477_478_487_488
+N,487,coef_N_477_478_487_488
+N,488,coef_N_477_478_487_488
+N,777,coef_N_777_778_788_888
+N,778,coef_N_777_778_788_888
+N,788,coef_N_777_778_788_888
+N,888,coef_N_777_778_788_888
+# cdap_final_rules,,
+M,5,coef_UNAVAILABLE
+M,4,coef_UNAVAILABLE
+# cdap_all_people,,
+M,***,coef_M_xxx
+N,***,coef_N_xxx
+H,***,coef_H_xxx
+M,****,coef_M_xxxx
+N,****,coef_N_xxxx
+H,****,coef_H_xxxx
+M,*****,coef_M_xxxxx
+N,*****,coef_N_xxxxx
+H,*****,coef_H_xxxxx
diff --git a/test/cdap/configs/cdap_joint_tour_coefficients.csv b/test/cdap/configs/cdap_joint_tour_coefficients.csv
new file mode 100644
index 000000000..14ee3fee3
--- /dev/null
+++ b/test/cdap/configs/cdap_joint_tour_coefficients.csv
@@ -0,0 +1,22 @@
+Label,description,Expression,dependency,coefficient
+,constant - joint tour,1,,-3.1506
+,joint tour for hhsize 2,hhsize==2,,0.331420519
+,joint tour for hhsize 3,hhsize==3,,0.360848095
+,joint tour for hhsize 4,hhsize==4,,-0.118640717
+,joint tour for hhsize 5+,hhsize>=5,,-0.053509306
+,person x is adult and DAP is M,ptype_px<7,M_px,0.008
+,person x is adult and DAP is N,ptype_px<7,N_px,1.2557
+,person x is kid and DAP is M,ptype_px>6,M_px,0.1088
+,person x is kid and DAP is N,ptype_px>6,N_px,1.6898
+,Accessibility to retail employment/Non-Mandatory Attractions for Joint Tour,(autos_p1<num_workers_p1)*nonmandatory_hov0_accessibility_p1,,0.055031985
+,Accessibility to retail employment/Non-Mandatory Attractions for Joint Tour,(autos_p1==num_workers_p1)*nonmandatory_hov1_accessibility_p1,,0.055031985
+,Accessibility to retail employment/Non-Mandatory Attractions for Joint Tour,(autos_p1>num_workers_p1)*nonmandatory_hov2_accessibility_p1,,0.055031985
+,Income less than $30k,income_p1<30000,,-0.192506367
+,Income between $60k and $100k,(income_p1>=60000)&(income_p1<=100000),,0.104325349
+,Income more than $100k,income_p1>100000,,0.104325349
+,No Car Households,autos_p1==0,,0
+,Cars Less than Workers,autos_p1<num_workers_p1,,0.088402389
+,Cars More than Workers,autos_p1>num_workers_p1,,-0.005896499
+,Constant - Joint Tour - MTC TM2 Calibration,1,,-0.5918
+,WorkAccessForMandatoryDap,WorkLocationLogsum_px,M_px,0.17217579
+,If All Adults stay at Home/ None of the Adults have Dap 1 or 2,ptype_pxprod<7|ptype_pxprod>6,H_px,-0.988838929
diff --git a/test/cdap/configs/constants.yaml b/test/cdap/configs/constants.yaml
new file mode 100644
index 000000000..b0bd5a1f3
--- /dev/null
+++ b/test/cdap/configs/constants.yaml
@@ -0,0 +1,68 @@
+## ActivitySim
+## See full license in LICENSE.txt.
+
+
+#HHT_NONE: 0
+#HHT_FAMILY_MARRIED: 1
+#HHT_FAMILY_MALE: 2
+#HHT_FAMILY_FEMALE: 3
+#HHT_NONFAMILY_MALE_ALONE: 4
+#HHT_NONFAMILY_MALE_NOTALONE: 5
+#HHT_NONFAMILY_FEMALE_ALONE: 6
+#HHT_NONFAMILY_FEMALE_NOTALONE: 7
+
+# convenience for expression files
+HHT_NONFAMILY: [4, 5, 6, 7]
+HHT_FAMILY: [1, 2, 3]
+
+PSTUDENT_GRADE_OR_HIGH: 1
+PSTUDENT_UNIVERSITY: 2
+PSTUDENT_NOT: 3
+
+GRADE_SCHOOL_MAX_AGE: 14
+GRADE_SCHOOL_MIN_AGE: 5
+
+SCHOOL_SEGMENT_NONE: 0
+SCHOOL_SEGMENT_GRADE: 1
+SCHOOL_SEGMENT_HIGH: 2
+SCHOOL_SEGMENT_UNIV: 3
+
+INCOME_SEGMENT_LOW: 1
+INCOME_SEGMENT_MED: 2
+INCOME_SEGMENT_HIGH: 3
+INCOME_SEGMENT_VERYHIGH: 4
+
+PEMPLOY_FULL: 1
+PEMPLOY_PART: 2
+PEMPLOY_NOT: 3
+PEMPLOY_CHILD: 4
+
+PTYPE_FULL: &ptype_full 1
+PTYPE_PART: &ptype_part 2
+PTYPE_UNIVERSITY: &ptype_university 3
+PTYPE_NONWORK: &ptype_nonwork 4
+PTYPE_RETIRED: &ptype_retired 5
+PTYPE_DRIVING: &ptype_driving 6
+PTYPE_SCHOOL: &ptype_school 7
+PTYPE_PRESCHOOL:  &ptype_preschool 8
+
+# these appear as column headers in non_mandatory_tour_frequency.csv
+PTYPE_NAME:
+  *ptype_full: PTYPE_FULL
+  *ptype_part: PTYPE_PART
+  *ptype_university: PTYPE_UNIVERSITY
+  *ptype_nonwork: PTYPE_NONWORK
+  *ptype_retired: PTYPE_RETIRED
+  *ptype_driving: PTYPE_DRIVING
+  *ptype_school: PTYPE_SCHOOL
+  *ptype_preschool: PTYPE_PRESCHOOL
+
+
+CDAP_ACTIVITY_MANDATORY: M
+CDAP_ACTIVITY_NONMANDATORY: N
+CDAP_ACTIVITY_HOME: H
+
+# Correction for transit skim expressions
+# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and
+# therefore need to be divided by the scale factor if used in expressions
+TRANSIT_SCALE_FACTOR: 100
diff --git a/test/cdap/configs/initialize_households.yaml b/test/cdap/configs/initialize_households.yaml
new file mode 100644
index 000000000..5d98a703d
--- /dev/null
+++ b/test/cdap/configs/initialize_households.yaml
@@ -0,0 +1,37 @@
+annotate_tables:
+  - tablename: persons
+    column_map:
+      HHID: household_id
+      PERID: person_id
+      AGEP: age
+      SEX: sex
+      SCHL: education_attainment
+      OCCP: occupation
+      WKHP: hours_worked
+      WKW: weeks_worked
+      EMPLOYED: employment_status
+      ESR: esr
+      SCHG: grade_attending
+    annotate:
+      SPEC: annotate_persons
+      DF: persons
+      TABLES:
+        - households
+  - tablename: households
+    column_map:
+      HHID: household_id
+      MAZ: home_zone_id
+      HHINCADJ: income
+      NWRKRS_ESR: num_workers
+      VEH: auto_ownership
+      NP: hhsize
+      HHT: hh_type
+      BLD: building_size
+      TYPE: hh_unit_type
+      MTCCountyID: county_id
+    annotate:
+      SPEC: annotate_households
+      DF: households
+      TABLES:
+        - persons
+        - land_use
\ No newline at end of file
diff --git a/test/cdap/configs/initialize_landuse.yaml b/test/cdap/configs/initialize_landuse.yaml
new file mode 100644
index 000000000..7cc1d7e34
--- /dev/null
+++ b/test/cdap/configs/initialize_landuse.yaml
@@ -0,0 +1,54 @@
+annotate_tables:
+  - tablename: land_use
+    column_map:
+        MAZ_ORIGINAL: zone_id
+        CountyID: county_id
+        DistID: DISTRICT
+        HH: TOTHH
+        POP: TOTPOP
+        ACRES: TOTACRE
+        emp_total: TOTEMP
+    annotate:
+      SPEC: annotate_landuse
+      DF: land_use
+  - tablename: accessibility
+    column_map:
+        column_1: nonmandatory_auto_accessibility
+        column_2: nonmandatory_transit_accessibility
+        column_3: nonmandatory_nm_accessibility
+        column_4: nonmandatory_sov0_accessibility
+        column_5: nonmandatory_sov1_accessibility
+        column_6: nonmandatory_sov2_accessibility
+        column_7: nonmandatory_hov0_accessibility
+        column_8: nonmandatory_hov1_accessibility
+        column_9: nonmandatory_hov2_accessibility
+        column_10: shop_hov_insufficient_accessibility
+        column_11: shop_hov_sufficient_accessibility
+        column_12: shop_hov_oversufficient_accessibility
+        column_13: maint_hov_insufficient_accessibility
+        column_14: maint_hov_sufficient_accessibility
+        column_15: maint_hov_oversufficient_accessibility
+        column_16: eat_hov_insufficient_accessibility
+        column_17: eat_hov_sufficient_accessibility
+        column_18: eat_hov_oversufficient_accessibility
+        column_19: visit_hov_insufficient_accessibility
+        column_20: visit_hov_sufficient_accessibility
+        column_21: visit_hov_oversufficient_accessibility
+        column_22: discr_hov_insufficient_accessibility
+        column_23: discr_hov_sufficient_accessibility
+        column_24: discr_hov_oversufficient_accessibility
+        column_25: escort_hov_insufficient_accessibility
+        column_26: escort_hov_sufficient_accessibility
+        column_27: escort_hov_oversufficient_accessibility
+        column_28: shop_sov_insufficient_accessibility
+        column_29: shop_sov_sufficient_accessibility
+        column_30: shop_sov_oversufficient_accessibility
+        column_31: maint_sov_insufficient_accessibility
+        column_32: maint_sov_sufficient_accessibility
+        column_33: maint_sov_oversufficient_accessibility
+        column_40: discr_sov_insufficient_accessibility
+        column_41: discr_sov_sufficient_accessibility
+        column_42: discr_sov_oversufficient_accessibility
+        column_45: total_emp_accessibility
+        column_47: hh_walktransit_accessibility
+        mgra : zone_id
\ No newline at end of file
diff --git a/test/cdap/configs/network_los.yaml b/test/cdap/configs/network_los.yaml
new file mode 100644
index 000000000..391125a38
--- /dev/null
+++ b/test/cdap/configs/network_los.yaml
@@ -0,0 +1,14 @@
+# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
+read_skim_cache: False
+# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
+write_skim_cache: True
+
+zone_system: 1
+
+taz_skims: skims.omx
+
+skim_time_periods:
+    time_window: 1440
+    period_minutes: 60
+    periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59
+    labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV']
\ No newline at end of file
diff --git a/test/cdap/configs/settings.yaml b/test/cdap/configs/settings.yaml
new file mode 100644
index 000000000..bd0f4acc9
--- /dev/null
+++ b/test/cdap/configs/settings.yaml
@@ -0,0 +1,60 @@
+# input tables
+input_table_list:
+  - tablename: households
+    filename: households.csv
+    index_col: household_id
+    rename_columns:
+      unique_hh_id: household_id
+      NP: hhsize
+      hh_workers_from_esr: num_workers
+      VEH: auto_ownership
+      MAZ: home_zone_id
+      HINCP: income
+    keep_columns:
+      - home_zone_id
+      - income
+      - hhsize
+      - HHT
+      - auto_ownership
+      - num_workers
+  - tablename: persons
+    filename: persons.csv
+    index_col: person_id
+    rename_columns:
+      unique_hh_id: household_id
+      AGEP: age
+      SPORDER: PNUM
+      SEX: sex
+      employed: pemploy
+      student_status: pstudent
+      person_type: ptype
+    keep_columns:
+      - household_id
+      - age
+      - PNUM
+      - sex
+      - pemploy
+      - pstudent
+      - ptype
+  - tablename: land_use
+    filename: land_use.csv
+    index_col: zone_id
+    rename_columns:
+      MAZ_ORIGINAL: zone_id
+      CountyID: county_id
+      TAZ_ORIGINAL: TAZ
+      DistID: DISTRICT
+      HH: TOTHH
+      POP: TOTPOP
+      ACRES: TOTACRE
+      emp_total: TOTEMP
+    keep_columns:
+      - TAZ
+      - DISTRICT
+      - SD
+      - county_id
+      - TOTHH
+      - TOTPOP
+      - TOTACRE
+
+trace_hh_id: 2
\ No newline at end of file
diff --git a/test/cdap/configs/tour_departure_and_duration_alternatives.csv b/test/cdap/configs/tour_departure_and_duration_alternatives.csv
new file mode 100644
index 000000000..bddab06b9
--- /dev/null
+++ b/test/cdap/configs/tour_departure_and_duration_alternatives.csv
@@ -0,0 +1,191 @@
+start,end
+5,5
+5,6
+5,7
+5,8
+5,9
+5,10
+5,11
+5,12
+5,13
+5,14
+5,15
+5,16
+5,17
+5,18
+5,19
+5,20
+5,21
+5,22
+5,23
+6,6
+6,7
+6,8
+6,9
+6,10
+6,11
+6,12
+6,13
+6,14
+6,15
+6,16
+6,17
+6,18
+6,19
+6,20
+6,21
+6,22
+6,23
+7,7
+7,8
+7,9
+7,10
+7,11
+7,12
+7,13
+7,14
+7,15
+7,16
+7,17
+7,18
+7,19
+7,20
+7,21
+7,22
+7,23
+8,8
+8,9
+8,10
+8,11
+8,12
+8,13
+8,14
+8,15
+8,16
+8,17
+8,18
+8,19
+8,20
+8,21
+8,22
+8,23
+9,9
+9,10
+9,11
+9,12
+9,13
+9,14
+9,15
+9,16
+9,17
+9,18
+9,19
+9,20
+9,21
+9,22
+9,23
+10,10
+10,11
+10,12
+10,13
+10,14
+10,15
+10,16
+10,17
+10,18
+10,19
+10,20
+10,21
+10,22
+10,23
+11,11
+11,12
+11,13
+11,14
+11,15
+11,16
+11,17
+11,18
+11,19
+11,20
+11,21
+11,22
+11,23
+12,12
+12,13
+12,14
+12,15
+12,16
+12,17
+12,18
+12,19
+12,20
+12,21
+12,22
+12,23
+13,13
+13,14
+13,15
+13,16
+13,17
+13,18
+13,19
+13,20
+13,21
+13,22
+13,23
+14,14
+14,15
+14,16
+14,17
+14,18
+14,19
+14,20
+14,21
+14,22
+14,23
+15,15
+15,16
+15,17
+15,18
+15,19
+15,20
+15,21
+15,22
+15,23
+16,16
+16,17
+16,18
+16,19
+16,20
+16,21
+16,22
+16,23
+17,17
+17,18
+17,19
+17,20
+17,21
+17,22
+17,23
+18,18
+18,19
+18,20
+18,21
+18,22
+18,23
+19,19
+19,20
+19,21
+19,22
+19,23
+20,20
+20,21
+20,22
+20,23
+21,21
+21,22
+21,23
+22,22
+22,23
+23,23
\ No newline at end of file
diff --git a/test/cdap/data/.gitkeep b/test/cdap/data/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/cdap/test_cdap.py b/test/cdap/test_cdap.py
new file mode 100644
index 000000000..df74740b4
--- /dev/null
+++ b/test/cdap/test_cdap.py
@@ -0,0 +1,413 @@
+import logging
+import pytest
+import os
+import shutil
+import pandas as pd
+import numpy as np
+from numpy import dot
+from numpy.linalg import norm
+
+# import models is necessary to initalize the model steps with orca
+from activitysim.abm import models
+from activitysim.core import pipeline, config
+from activitysim.core import tracing
+
+logger = logging.getLogger(__name__)
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def module() -> str:
+    """
+    A pytest fixture that returns the data folder location.
+    :return: folder location for any necessary data to initialize the tests
+    """
+    return "cdap"
+
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def tables(prepare_module_inputs) -> dict[str, str]:
+    """
+    A pytest fixture that returns the "mock" tables to build pipeline dataframes. The
+    key-value pair is the name of the table and the index column.
+    :return: dict
+    """
+    return {
+        "land_use": "MAZ_ORIGINAL",
+        "persons": "person_id",
+        "households": "household_id",
+        "accessibility": "MAZ_ORIGINAL",
+    }
+
+
+# Used by conftest.py initialize_pipeline method
+# Set to true if you need to read skims into the pipeline
+@pytest.fixture(scope="module")
+def initialize_network_los() -> bool:
+    """
+    A pytest boolean fixture indicating whether network skims should be read from the
+    fixtures test data folder.
+    :return: bool
+    """
+    return False
+
+
+@pytest.fixture(scope="module")
+def load_checkpoint() -> bool:
+    """
+    checkpoint to be loaded from the pipeline when reconnecting.
+    """
+    return "initialize_households"
+
+
+# make a reconnect_pipeline internal to cdap
+@pytest.mark.skipif(
+    os.path.isfile("test/cdap/output/pipeline.h5"),
+    reason="no need to recreate pipeline store if alreayd exist",
+)
+def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog):
+    # Run summarize model
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(models=["initialize_landuse", "initialize_households"])
+
+    pipeline.close_pipeline()
+
+
+def test_cdap_from_pipeline(reconnect_pipeline: pipeline.Pipeline, caplog):
+    # Run summarize model
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(models=["cdap_simulate"], resume_after="initialize_households")
+
+    # get the updated pipeline data
+    person_df = pipeline.get_table("persons")
+
+    # get the updated pipeline data
+    household_df = pipeline.get_table("households")
+
+    ############################
+    # person cdap pattern validation
+    ############################
+    # cdap person result from the model
+    logger.info("person cdap pattern validation")
+
+    target_key = "activity_pattern"
+    simulated_key = "cdap_activity"
+    similarity_threshold = 0.99
+
+    simulated_df = create_summary(
+        person_df, key=simulated_key, out_col="Simulated_Share"
+    )
+
+    # cdap person result from the TM2 run
+    target_df = create_summary(person_df, key=target_key, out_col="Target_Share")
+
+    # compare simulated and target results
+    similarity_value = compare_simulated_against_target(
+        target_df, simulated_df, target_key, simulated_key
+    )
+
+    # if the cosine_similarity >= threshold then the simulated and target results are "similar"
+    assert similarity_value >= similarity_threshold
+
+    ############################
+    # household joint pattern validation
+    ############################
+    # cdap household result from the model
+    logger.info("household joint vs non-joint tour validation")
+
+    target_key = "has_joint_tour_tm2"
+    simulated_key = "has_joint_tour"
+    similarity_threshold = 0.99
+
+    simulated_df = create_summary(
+        household_df, key=simulated_key, out_col="Simulated_Share"
+    )
+
+    # cdap household result from the TM2 run
+    household_df["has_joint_tour_tm2"] = np.where(
+        household_df["cdap_pattern"].str.endswith("j"), 1, 0
+    )
+    target_df = create_summary(household_df, key=target_key, out_col="Target_Share")
+
+    # compare simulated and target results
+    similarity_value = compare_simulated_against_target(
+        target_df, simulated_df, target_key, simulated_key
+    )
+
+    # if the cosine_similarity >= threshold then the simulated and target results are "similar"
+    assert similarity_value >= similarity_threshold
+
+    ############################
+    # one person household cdap pattern validation
+    ############################
+    # cdap household result from the model
+    logger.info("one-person household cdap pattern validation")
+
+    target_key = "cdap_pattern"
+    simulated_key = "cdap_activity"
+    similarity_threshold = 0.99
+
+    household_df["cdap_pattern"] = household_df["cdap_pattern"].apply(
+        lambda x: x[:-1].upper() if x.endswith("0") else x.upper()
+    )
+    household_df["cdap_activity"] = household_df.apply(
+        lambda x: x["cdap_activity"] + "J"
+        if x["has_joint_tour"] == 1
+        else x["cdap_activity"],
+        axis=1,
+    )
+
+    hh1_df = household_df[household_df.hhsize == 1].copy()
+
+    simulated_df = create_summary(hh1_df, key=simulated_key, out_col="Simulated_Share")
+    target_df = create_summary(hh1_df, key=target_key, out_col="Target_Share")
+
+    # compare simulated and target results
+    similarity_value = compare_simulated_against_target(
+        target_df, simulated_df, target_key, simulated_key
+    )
+
+    # if the cosine_similarity >= threshold then the simulated and target results are "similar"
+    assert similarity_value >= similarity_threshold
+
+    ############################
+    # two person household cdap pattern validation
+    ############################
+    # cdap household result from the model
+    logger.info("two-person household cdap pattern validation")
+
+    target_key = "cdap_pattern"
+    simulated_key = "cdap_activity"
+    similarity_threshold = 0.99
+
+    hh2_df = household_df[household_df.hhsize == 2].copy()
+
+    simulated_df = create_summary(hh2_df, key=simulated_key, out_col="Simulated_Share")
+    target_df = create_summary(hh2_df, key=target_key, out_col="Target_Share")
+
+    # compare simulated and target results
+    similarity_value = compare_simulated_against_target(
+        target_df, simulated_df, target_key, simulated_key
+    )
+
+    # if the cosine_similarity >= threshold then the simulated and target results are "similar"
+    assert similarity_value >= similarity_threshold
+
+    ############################
+    # three person household cdap pattern validation
+    ############################
+    # cdap household result from the model
+    logger.info("three-person household cdap pattern validation")
+
+    target_key = "cdap_pattern"
+    simulated_key = "cdap_activity"
+    similarity_threshold = 0.99
+
+    hh3_df = household_df[household_df.hhsize == 3].copy()
+
+    simulated_df = create_summary(hh3_df, key=simulated_key, out_col="Simulated_Share")
+    target_df = create_summary(hh3_df, key=target_key, out_col="Target_Share")
+
+    # compare simulated and target results
+    similarity_value = compare_simulated_against_target(
+        target_df, simulated_df, target_key, simulated_key
+    )
+
+    # if the cosine_similarity >= threshold then the simulated and target results are "similar"
+    assert similarity_value >= similarity_threshold
+
+
+# fetch/prepare existing files for model inputs
+# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim
+@pytest.fixture(scope="module")
+def prepare_module_inputs() -> None:
+    """
+    copy input files from sharepoint into test folder
+
+    create unique person id in person file
+
+    :return: None
+    """
+    # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/
+    # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1
+    accessibility_file = os.path.join(
+        "test", "cdap", "data", "tm2_outputs", "accessibilities.csv"
+    )
+    household_file = os.path.join("test", "cdap", "data", "popsyn", "households.csv")
+    person_file = os.path.join("test", "cdap", "data", "popsyn", "persons.csv")
+    landuse_file = os.path.join(
+        "test", "cdap", "data", "landuse", "maz_data_withDensity.csv"
+    )
+
+    test_dir = os.path.join("test", "cdap", "data")
+
+    shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv"))
+    shutil.copy(household_file, os.path.join(test_dir, "households.csv"))
+    shutil.copy(person_file, os.path.join(test_dir, "persons.csv"))
+    shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv"))
+
+    # add original maz id to accessibility table
+    land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv"))
+    accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv"))
+
+    accessibility_df = pd.merge(
+        accessibility_df,
+        land_use_df[["MAZ", "MAZ_ORIGINAL"]].rename(columns={"MAZ": "mgra"}),
+        how="left",
+        on="mgra",
+    )
+
+    accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False)
+
+    # currently household file has to have these two columns, even before annotation
+    # because annotate person happens before household and uses these two columns
+    # TODO find a way to get around this
+    ####
+    household_df = pd.read_csv(os.path.join(test_dir, "households.csv"))
+
+    household_columns_dict = {"HHID": "household_id", "MAZ": "home_zone_id"}
+
+    household_df.rename(columns=household_columns_dict, inplace=True)
+
+    tm2_simulated_household_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "householdData_3.csv")
+    )
+    tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    household_df = pd.merge(
+        household_df,
+        tm2_simulated_household_df[
+            [
+                "household_id",
+                "autos",
+                "automated_vehicles",
+                "transponder",
+                "cdap_pattern",
+                "jtf_choice",
+            ]
+        ],
+        how="inner",  # tm2 is not 100% sample run
+        on="household_id",
+    )
+
+    household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False)
+
+    person_df = pd.read_csv(os.path.join(test_dir, "persons.csv"))
+
+    person_columns_dict = {"HHID": "household_id", "PERID": "person_id"}
+
+    person_df.rename(columns=person_columns_dict, inplace=True)
+
+    tm2_simulated_person_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "personData_3.csv")
+    )
+    tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    person_df = pd.merge(
+        person_df,
+        tm2_simulated_person_df[
+            [
+                "household_id",
+                "person_id",
+                "type",
+                "value_of_time",
+                "activity_pattern",
+                "imf_choice",
+                "inmf_choice",
+                "fp_choice",
+                "reimb_pct",
+                "workDCLogsum",
+                "schoolDCLogsum",
+            ]
+        ],
+        how="inner",  # tm2 is not 100% sample run
+        on=["household_id", "person_id"],
+    )
+
+    # get tm2 simulated workplace and school location results
+    tm2_simulated_wsloc_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "wsLocResults_3.csv")
+    )
+    tm2_simulated_wsloc_df.rename(
+        columns={"HHID": "household_id", "PersonID": "person_id"}, inplace=True
+    )
+
+    person_df = pd.merge(
+        person_df,
+        tm2_simulated_wsloc_df[
+            [
+                "household_id",
+                "person_id",
+                "WorkLocation",
+                "WorkLocationLogsum",  # this is the same as `workDCLogsum` in tm2 person output
+                "SchoolLocation",
+                "SchoolLocationLogsum",  # this is the same as `schoolDCLogsum` in tm2 person output
+            ]
+        ],
+        how="inner",  # ctramp might not be 100% sample run
+        on=["household_id", "person_id"],
+    )
+
+    person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False)
+    ####
+
+
+def create_summary(input_df, key, out_col="Share") -> pd.DataFrame:
+    """
+    Create summary for the input data.
+    1. group input data by the "key" column
+    2. calculate the percent of input data records in each "key" category.
+
+    :return: pd.DataFrame
+    """
+
+    out_df = input_df.groupby(key).size().reset_index(name="Count")
+    out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4)
+
+    return out_df[[key, out_col]]
+
+
+def cosine_similarity(a, b):
+    """
+    Computes cosine similarity between two vectors.
+
+    Cosine similarity is used here as a metric to measure similarity between two sequence of numbers.
+    Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them
+    i.e., dot products of the vectors divided by the product of their lengths.
+
+    :return:
+    """
+
+    return dot(a, b) / (norm(a) * norm(b))
+
+
+def compare_simulated_against_target(
+    target_df: pd.DataFrame,
+    simulated_df: pd.DataFrame,
+    target_key: str,
+    simulated_key: str,
+) -> bool:
+    """
+    compares the simulated and target results by computing the cosine similarity between them.
+
+    :return:
+    """
+
+    merged_df = pd.merge(
+        target_df, simulated_df, left_on=target_key, right_on=simulated_key, how="outer"
+    )
+    merged_df = merged_df.fillna(0)
+
+    logger.info("simulated vs target share:\n%s" % merged_df)
+
+    similarity_value = cosine_similarity(
+        merged_df["Target_Share"].tolist(), merged_df["Simulated_Share"].tolist()
+    )
+
+    logger.info("cosine similarity:\n%s" % similarity_value)
+
+    return similarity_value
diff --git a/test/conftest.py b/test/conftest.py
new file mode 100644
index 000000000..7d8b33203
--- /dev/null
+++ b/test/conftest.py
@@ -0,0 +1,105 @@
+import os
+
+import orca
+import pandas as pd
+import pytest
+from activitysim.core import pipeline
+from activitysim.core.los import Network_LOS as los
+
+
+@pytest.fixture(scope="module")
+def initialize_pipeline(
+    module: str, tables: dict[str, str], initialize_network_los: bool
+) -> pipeline.Pipeline:
+    test_dir = os.path.join("test", module)
+    configs_dir = os.path.join(test_dir, "configs")
+    data_dir = os.path.join(test_dir, "data")
+    output_dir = os.path.join(test_dir, "output")
+
+    if os.path.isdir(configs_dir):
+        orca.add_injectable("configs_dir", configs_dir)
+
+    if os.path.isdir(data_dir):
+        orca.add_injectable("data_dir", data_dir)
+
+    if os.path.isdir(test_dir):
+        if not os.path.isdir(output_dir):
+            os.mkdir(output_dir)
+        orca.add_injectable("output_dir", output_dir)
+
+    # Read in the input test dataframes
+    for dataframe_name, idx_name in tables.items():
+        df = pd.read_csv(
+            os.path.join("test", module, "data", f"{dataframe_name}.csv"),
+            index_col=idx_name,
+        )
+        orca.add_table(dataframe_name, df)
+
+    if initialize_network_los:
+        net_los = los()
+        net_los.load_data()
+        orca.add_injectable("network_los", net_los)
+
+    # Add an output directory in current working directory if it's not already there
+    try:
+        os.makedirs("output")
+    except FileExistsError:
+        # directory already exists
+        pass
+
+    # Add the dataframes to the pipeline
+    pipeline.open_pipeline()
+    pipeline.add_checkpoint(module)
+    pipeline.close_pipeline()
+
+    # By convention, this method needs to yield something
+    yield pipeline._PIPELINE
+
+    if pipeline.is_open():
+        pipeline.close_pipeline()
+
+
+@pytest.fixture(scope="module")
+def reconnect_pipeline(
+    module: str, initialize_network_los: bool, load_checkpoint: str
+) -> pipeline.Pipeline:
+    test_dir = os.path.join("test", module)
+    configs_dir = os.path.join(test_dir, "configs")
+    data_dir = os.path.join(test_dir, "data")
+    output_dir = os.path.join(test_dir, "output")
+
+    if os.path.isdir(configs_dir):
+        orca.add_injectable("configs_dir", configs_dir)
+
+    if os.path.isdir(data_dir):
+        orca.add_injectable("data_dir", data_dir)
+
+    if os.path.isdir(test_dir):
+        if not os.path.isdir(output_dir):
+            os.mkdir(output_dir)
+        orca.add_injectable("output_dir", output_dir)
+
+    # Read in the existing pipeline
+    orca.add_injectable("pipeline_file_path", output_dir)
+
+    if initialize_network_los:
+        net_los = los()
+        net_los.load_data()
+        orca.add_injectable("network_los", net_los)
+
+    # Add an output directory in current working directory if it's not already there
+    try:
+        os.makedirs("output")
+    except FileExistsError:
+        # directory already exists
+        pass
+
+    pipeline.open_pipeline(resume_after=load_checkpoint)
+    pipeline.add_checkpoint(module)
+    pipeline.close_pipeline()
+    # By convention, this method needs to yield something
+    yield pipeline._PIPELINE
+
+    # pytest teardown code
+    if pipeline.is_open():
+        pipeline.close_pipeline()
diff --git a/test/joint_tour_frequency/.gitkeep b/test/joint_tour_frequency/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/joint_tours/.gitkeep b/test/joint_tours/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/joint_tours/configs/JointTourFrequency.xls b/test/joint_tours/configs/JointTourFrequency.xls
new file mode 100644
index 000000000..2eb7699bb
Binary files /dev/null and b/test/joint_tours/configs/JointTourFrequency.xls differ
diff --git a/test/joint_tours/configs/annotate_households.csv b/test/joint_tours/configs/annotate_households.csv
new file mode 100644
index 000000000..eead2858c
--- /dev/null
+++ b/test/joint_tours/configs/annotate_households.csv
@@ -0,0 +1,19 @@
+Description,Target,Expression
+#,, annotate households table after import
+,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
+num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)"
+num_children,num_children,"_PERSON_COUNT('~adult', persons, households)"
+num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers
+participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0)
+number of travel active fulltime workers,num_travel_active_full_time_workers,"_PERSON_COUNT('(ptype == 1) & (cdap_activity != \'H\')', persons, households)"
+number of travel active parttime workers,num_travel_active_part_time_workers,"_PERSON_COUNT('(ptype == 2) & (cdap_activity != \'H\')', persons, households)"
+number of travel active university student,num_travel_active_university_students,"_PERSON_COUNT('(ptype == 3) & (cdap_activity != \'H\')', persons, households)"
+number of travel active non-workers,num_travel_active_non_workers,"_PERSON_COUNT('(ptype == 4) & (cdap_activity != \'H\')', persons, households)"
+number of travel active retirees,num_travel_active_retirees,"_PERSON_COUNT('(ptype == 5) & (cdap_activity != \'H\')', persons, households)"
+number of travel active driving age students,num_travel_active_driving_age_students,"_PERSON_COUNT('(ptype == 6) & (cdap_activity != \'H\')', persons, households)"
+number of travel active pre-driving age school kids,num_travel_active_pre_driving_age_school_kids,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons, households)"
+number of travel active pre-school kids,num_travel_active_pre_school_kids,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons, households)"
+number of travel active adults,num_travel_active_adults,"_PERSON_COUNT('adult & (cdap_activity != \'H\')', persons, households)"
+number of travel active chilren,num_travel_active_children,"_PERSON_COUNT('~adult & (cdap_activity != \'H\')', persons, households)"
\ No newline at end of file
diff --git a/test/joint_tours/configs/annotate_landuse.csv b/test/joint_tours/configs/annotate_landuse.csv
new file mode 100644
index 000000000..65237c0ad
--- /dev/null
+++ b/test/joint_tours/configs/annotate_landuse.csv
@@ -0,0 +1,6 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+household_density,household_density,land_use.TOTHH / land_use.TOTACRE
+employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE
+population_density,population_density,land_use.TOTPOP / land_use.TOTACRE
+density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
\ No newline at end of file
diff --git a/test/joint_tours/configs/annotate_persons.csv b/test/joint_tours/configs/annotate_persons.csv
new file mode 100644
index 000000000..6af8a61d7
--- /dev/null
+++ b/test/joint_tours/configs/annotate_persons.csv
@@ -0,0 +1,16 @@
+Description,Target,Expression
+#,, annotate persons table after import
+,ptype,0
+,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)"
+,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)"
+,ptype,"np.where(persons.type == 'University student', 3, ptype)"
+,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)"
+,ptype,"np.where(persons.type == 'Retired', 5, ptype)"
+,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)"
+,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)"
+,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)"
+home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)"
+travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME
+# adult is needed as it's harded coded in activitysim core overlap.py
+adult,adult,ptype<=6
+child,child,ptype>6
\ No newline at end of file
diff --git a/test/joint_tours/configs/annotate_persons_jtp.csv b/test/joint_tours/configs/annotate_persons_jtp.csv
new file mode 100644
index 000000000..a72c86605
--- /dev/null
+++ b/test/joint_tours/configs/annotate_persons_jtp.csv
@@ -0,0 +1,3 @@
+Description,Target,Expression
+#,, annotate persons table after joint_tour_participation model has run
+num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)"
diff --git a/test/joint_tours/configs/annotate_tours.csv b/test/joint_tours/configs/annotate_tours.csv
new file mode 100644
index 000000000..225abe7f8
--- /dev/null
+++ b/test/joint_tours/configs/annotate_tours.csv
@@ -0,0 +1,6 @@
+Description,Target,Expression
+,tour_category,tours.tour_category.str.lower()
+,tour_purpose,tours.tour_purpose.str.lower()
+,tour_type,tours.tour_purpose.str.lower()
+,origin,tours.orig_mgra
+,destination,tours.dest_mgra
diff --git a/test/joint_tours/configs/constants.yaml b/test/joint_tours/configs/constants.yaml
new file mode 100644
index 000000000..b0bd5a1f3
--- /dev/null
+++ b/test/joint_tours/configs/constants.yaml
@@ -0,0 +1,68 @@
+## ActivitySim
+## See full license in LICENSE.txt.
+
+
+#HHT_NONE: 0
+#HHT_FAMILY_MARRIED: 1
+#HHT_FAMILY_MALE: 2
+#HHT_FAMILY_FEMALE: 3
+#HHT_NONFAMILY_MALE_ALONE: 4
+#HHT_NONFAMILY_MALE_NOTALONE: 5
+#HHT_NONFAMILY_FEMALE_ALONE: 6
+#HHT_NONFAMILY_FEMALE_NOTALONE: 7
+
+# convenience for expression files
+HHT_NONFAMILY: [4, 5, 6, 7]
+HHT_FAMILY: [1, 2, 3]
+
+PSTUDENT_GRADE_OR_HIGH: 1
+PSTUDENT_UNIVERSITY: 2
+PSTUDENT_NOT: 3
+
+GRADE_SCHOOL_MAX_AGE: 14
+GRADE_SCHOOL_MIN_AGE: 5
+
+SCHOOL_SEGMENT_NONE: 0
+SCHOOL_SEGMENT_GRADE: 1
+SCHOOL_SEGMENT_HIGH: 2
+SCHOOL_SEGMENT_UNIV: 3
+
+INCOME_SEGMENT_LOW: 1
+INCOME_SEGMENT_MED: 2
+INCOME_SEGMENT_HIGH: 3
+INCOME_SEGMENT_VERYHIGH: 4
+
+PEMPLOY_FULL: 1
+PEMPLOY_PART: 2
+PEMPLOY_NOT: 3
+PEMPLOY_CHILD: 4
+
+PTYPE_FULL: &ptype_full 1
+PTYPE_PART: &ptype_part 2
+PTYPE_UNIVERSITY: &ptype_university 3
+PTYPE_NONWORK: &ptype_nonwork 4
+PTYPE_RETIRED: &ptype_retired 5
+PTYPE_DRIVING: &ptype_driving 6
+PTYPE_SCHOOL: &ptype_school 7
+PTYPE_PRESCHOOL:  &ptype_preschool 8
+
+# these appear as column headers in non_mandatory_tour_frequency.csv
+PTYPE_NAME:
+  *ptype_full: PTYPE_FULL
+  *ptype_part: PTYPE_PART
+  *ptype_university: PTYPE_UNIVERSITY
+  *ptype_nonwork: PTYPE_NONWORK
+  *ptype_retired: PTYPE_RETIRED
+  *ptype_driving: PTYPE_DRIVING
+  *ptype_school: PTYPE_SCHOOL
+  *ptype_preschool: PTYPE_PRESCHOOL
+
+
+CDAP_ACTIVITY_MANDATORY: M
+CDAP_ACTIVITY_NONMANDATORY: N
+CDAP_ACTIVITY_HOME: H
+
+# Correction for transit skim expressions
+# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and
+# therefore need to be divided by the scale factor if used in expressions
+TRANSIT_SCALE_FACTOR: 100
diff --git a/test/joint_tours/configs/initialize_households.yaml b/test/joint_tours/configs/initialize_households.yaml
new file mode 100644
index 000000000..a5cabc3df
--- /dev/null
+++ b/test/joint_tours/configs/initialize_households.yaml
@@ -0,0 +1,38 @@
+annotate_tables:
+  - tablename: persons
+    column_map:
+      HHID: household_id
+      PERID: person_id
+      AGEP: age
+      SEX: sex
+      SCHL: education_attainment
+      OCCP: occupation
+      WKHP: hours_worked
+      WKW: weeks_worked
+      EMPLOYED: employment_status
+      ESR: esr
+      SCHG: grade_attending
+      activity_pattern: cdap_activity
+    annotate:
+      SPEC: annotate_persons
+      DF: persons
+      TABLES:
+        - households
+  - tablename: households
+    column_map:
+      HHID: household_id
+      MAZ: home_zone_id
+      HHINCADJ: income
+      NWRKRS_ESR: num_workers
+      VEH: auto_ownership
+      NP: hhsize
+      HHT: hh_type
+      BLD: building_size
+      TYPE: hh_unit_type
+      MTCCountyID: county_id
+    annotate:
+      SPEC: annotate_households
+      DF: households
+      TABLES:
+        - persons
+        - land_use
\ No newline at end of file
diff --git a/test/joint_tours/configs/initialize_landuse.yaml b/test/joint_tours/configs/initialize_landuse.yaml
new file mode 100644
index 000000000..7cc1d7e34
--- /dev/null
+++ b/test/joint_tours/configs/initialize_landuse.yaml
@@ -0,0 +1,54 @@
+annotate_tables:
+  - tablename: land_use
+    column_map:
+        MAZ_ORIGINAL: zone_id
+        CountyID: county_id
+        DistID: DISTRICT
+        HH: TOTHH
+        POP: TOTPOP
+        ACRES: TOTACRE
+        emp_total: TOTEMP
+    annotate:
+      SPEC: annotate_landuse
+      DF: land_use
+  - tablename: accessibility
+    column_map:
+        column_1: nonmandatory_auto_accessibility
+        column_2: nonmandatory_transit_accessibility
+        column_3: nonmandatory_nm_accessibility
+        column_4: nonmandatory_sov0_accessibility
+        column_5: nonmandatory_sov1_accessibility
+        column_6: nonmandatory_sov2_accessibility
+        column_7: nonmandatory_hov0_accessibility
+        column_8: nonmandatory_hov1_accessibility
+        column_9: nonmandatory_hov2_accessibility
+        column_10: shop_hov_insufficient_accessibility
+        column_11: shop_hov_sufficient_accessibility
+        column_12: shop_hov_oversufficient_accessibility
+        column_13: maint_hov_insufficient_accessibility
+        column_14: maint_hov_sufficient_accessibility
+        column_15: maint_hov_oversufficient_accessibility
+        column_16: eat_hov_insufficient_accessibility
+        column_17: eat_hov_sufficient_accessibility
+        column_18: eat_hov_oversufficient_accessibility
+        column_19: visit_hov_insufficient_accessibility
+        column_20: visit_hov_sufficient_accessibility
+        column_21: visit_hov_oversufficient_accessibility
+        column_22: discr_hov_insufficient_accessibility
+        column_23: discr_hov_sufficient_accessibility
+        column_24: discr_hov_oversufficient_accessibility
+        column_25: escort_hov_insufficient_accessibility
+        column_26: escort_hov_sufficient_accessibility
+        column_27: escort_hov_oversufficient_accessibility
+        column_28: shop_sov_insufficient_accessibility
+        column_29: shop_sov_sufficient_accessibility
+        column_30: shop_sov_oversufficient_accessibility
+        column_31: maint_sov_insufficient_accessibility
+        column_32: maint_sov_sufficient_accessibility
+        column_33: maint_sov_oversufficient_accessibility
+        column_40: discr_sov_insufficient_accessibility
+        column_41: discr_sov_sufficient_accessibility
+        column_42: discr_sov_oversufficient_accessibility
+        column_45: total_emp_accessibility
+        column_47: hh_walktransit_accessibility
+        mgra : zone_id
\ No newline at end of file
diff --git a/test/joint_tours/configs/initialize_tours.yaml b/test/joint_tours/configs/initialize_tours.yaml
new file mode 100644
index 000000000..999382c62
--- /dev/null
+++ b/test/joint_tours/configs/initialize_tours.yaml
@@ -0,0 +1,6 @@
+#
+annotate_tours:
+  SPEC: annotate_tours
+  DF: tours
+  TABLES:
+    - land_use
\ No newline at end of file
diff --git a/test/joint_tours/configs/joint_tour_frequency_composition.csv b/test/joint_tours/configs/joint_tour_frequency_composition.csv
new file mode 100644
index 000000000..2cace7b25
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_frequency_composition.csv
@@ -0,0 +1,99 @@
+Label,Description,Expression,Coefficient
+,Constant for shopping tour,@df.shopping,coef_constant_for_shopping_tour
+,Constant for Maintenance tour,@df.othmaint,coef_constant_for_maintenance_tour
+,Constant for eating out tour,@df.eatout,coef_constant_for_eating_out_tour
+,Constant for visiting tour,@df.social,coef_constant_for_visiting_tour
+,Constant for discretionary tour,@df.othdiscr,coef_constant_for_discretionary_tour
+,Constant for 2 shopping tour,@df.shopping==2,coef_constant_for_2_shopping_tour
+,1 Shopping and 1 Maintenance Tour,@((df.shopping==1) & (df.othmaint==1)),coef_1_shopping_and_1_maintenance_tour
+,1 Shopping and 1 Eating Out Tour,@((df.shopping==1) & (df.eatout==1)),coef_1_shopping_and_1_eating_out_tour
+,1 Shopping and 1 Visiting Tour,@((df.shopping==1) & (df.social==1)),coef_1_shopping_and_1_visiting_tour
+,1 Shopping and 1 Discretionary Tour,@((df.shopping==1) & (df.othdiscr==1)),coef_1_shopping_and_1_discretionary_tour
+,Constant for 2 Maintenance tour,@df.othmaint==2,coef_constant_for_2_maintenance_tour
+,1 Maintenance and 1 Eating Out Tour,@((df.othmaint==1) & (df.eatout==1)),coef_1_maintenance_and_1_eating_out_tour
+,1 Maintenance and 1 Visiting Tour,@((df.othmaint==1) & (df.social==1)),coef_1_maintenance_and_1_visiting_tour
+,1 Maintenance and 1 Discretionary Tour,@((df.othmaint==1) & (df.othdiscr==1)),coef_1_maintenance_and_1_discretionary_tour
+,Constant for 2 eating out tour,@df.eatout==2,coef_constant_for_2_eating_out_tour
+,1 Eating Out and 1 Visiting Tour,@((df.eatout==1) & (df.social==1)),coef_1_eating_out_and_1_visiting_tour
+,1 Eating Out and 1 Discretionary Tour,@((df.eatout==1) & (df.othdiscr==1)),coef_1_eating_out_and_1_discretionary_tour
+,Constant for 2 visiting tour,@df.social==2,coef_constant_for_2_visiting_tour
+,1 Visiting and 1 Discretionary Tour,@((df.social==1) & (df.othdiscr==1)),coef_1_visiting_and_1_discretionary_tour
+,Constant for 2 discretionary tour,othdiscr==2,coef_constant_for_2_discretionary_tour
+,Number of Active Full time workers /Shopping,num_travel_active_full_time_workers * shopping,coef_number_of_active_full_time_workers_shopping
+,Number of Active Non-workers /Shopping,num_travel_active_non_workers * shopping,coef_number_of_active_nonworkers_shopping
+,Number of Active Pre- Driving Age School Children /Shopping,num_travel_active_pre_driving_age_school_kids * shopping,coef_number_of_active_pre_driving_age_school_children_shopping
+,Number of Active Preschool Children /Shopping,num_travel_active_pre_school_kids * shopping,coef_number_of_active_preschool_children_shopping
+,Number of Active Non-workers /Maintenance,num_travel_active_non_workers * othmaint,coef_number_of_active_nonworkers_maintenance
+,Number of Active Retirees /Maintenance,num_travel_active_retirees * othmaint,coef_number_of_active_retirees_maintenance
+,Number of Active Driving Age School Children /Maintenance,num_travel_active_driving_age_students * othmaint,coef_number_of_active_driving_age_school_children_maintenance
+,Number of Active Preschool Children /Maintenance,num_travel_active_pre_school_kids * othmaint,coef_number_of_active_preschool_children_maintenance
+,Number of Active Full time workers /Eating Out,num_travel_active_full_time_workers * eatout,coef_number_of_active_full_time_workers_eating_out
+,Number of Active University Students /Eating Out,num_travel_active_university_students * eatout,coef_number_of_active_university_students_eating_out
+,Number of Active Retirees /Eating Out,num_travel_active_retirees * eatout,coef_number_of_active_retirees_eating_out
+,Number of Active Pre- Driving Age School Children /Eating Out,num_travel_active_pre_driving_age_school_kids * eatout,coef_number_of_active_pre_driving_age_school_children_eating_out
+,Number of Active Preschool Children /Eating Out,num_travel_active_pre_school_kids * eatout,coef_number_of_active_preschool_children_eating_out
+,Number of Active Pre- Driving Age School Children /Visiting,num_travel_active_pre_driving_age_school_kids * social,coef_number_of_active_pre_driving_age_school_children_visiting
+,Number of Active Preschool Children /Visiting,num_travel_active_pre_school_kids * social,coef_number_of_active_preschool_children_visiting
+,Number of Active Part time workers /Discretionary,num_travel_active_part_time_workers * othdiscr,coef_number_of_active_part_time_workers_discretionary
+,Number of Active University Students /Discretionary,num_travel_active_university_students * othdiscr,coef_number_of_active_university_students_discretionary
+,Number of Active Driving Age School Children /Discretionary,num_travel_active_driving_age_students * othdiscr,coef_number_of_active_driving_age_school_children_discretionary
+,Number of Active Preschool Children /Discretionary,num_travel_active_pre_school_kids * othdiscr,coef_number_of_active_preschool_children_discretionary
+,HH has more autos than workers/ Maintenance,(auto_ownership > num_workers) * othmaint,coef_hh_has_more_autos_than_workers_maintenance
+,Income > $100k /Maintenance,(income>100000) * othmaint,coef_income_gtr_100k_maintenance
+,"Income Less than $29,999 / Eating Out",(income<30000) * eatout,coef_income_less_than_29999__eating_out
+,"Income Between $30,000 to $59,999 / Eating Out",((income>=30000) & (income<60000)) * othmaint,coef_income_between_30000_to_59999__eating_out
+,"Income Less than $29,999 / Discretionary",(income<30000) * othdiscr,coef_income_less_than_29999__discretionary
+,"Income Between $30,000 to $59,999 / Discretionary",((income>=30000) & (income<60000)) * othdiscr,coef_income_between_30000_to_59999__discretionary
+,Shopping HOV accessibility for 2 Tours,((autos<num_workers)*shop_hov_insufficient_accessibility+(autos==num_workers)*shop_hov_sufficient_accessibility+(autos>num_workers)*shop_hov_oversufficient_accessibility)*(num_joint_tours==2)*shopping,coef_shopping_hov_accessibility_for_2_tours
+,Maintenance HOV Accessibility,((autos<num_workers)*maint_hov_insufficient_accessibility+(autos==num_workers)*maint_hov_sufficient_accessibility+(autos>num_workers)*maint_hov_oversufficient_accessibility)*othmaint,coef_maintenance_hov_accessibility
+,Discretionary HOV Accessibility,((autos<num_workers)*discr_hov_insufficient_accessibility+(autos==num_workers)*discr_hov_sufficient_accessibility+(autos>num_workers)*discr_hov_oversufficient_accessibility)*othdiscr,coef_discretionary_hov_accessibility
+,Constant for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_constant_for_children_party_shopping_tour
+,Constant for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_constant_for_children_party_maintenance_tour
+,Constant for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_constant_for_children_party_eating_out_tour
+,Constant for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_constant_for_children_party_visiting_tour
+,Constant for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_constant_for_children_party_discretionary_tour
+,Constant for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_constant_for_mixed_party_shopping_tour
+,Constant for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_constant_for_mixed_party_maintenance_tour
+,Constant for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_constant_for_mixed_party_eating_out_tour
+,Constant for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_constant_for_mixed_party_visiting_tour
+,Constant for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_constant_for_mixed_party_discretionary_tour
+,Number of Active Full time workers /Adult Party,num_travel_active_full_time_workers * (party1==1) + num_travel_active_full_time_workers * (party2==1),coef_number_of_active_full_time_workers_adult_party
+,Number of Active Part time workers /Adult Party,num_travel_active_part_time_workers * (party1==1) + num_travel_active_part_time_workers * (party2==1),coef_number_of_active_part_time_workers_adult_party
+,Number of Active University Students /Adult Party,num_travel_active_university_students * (party1==1) + num_travel_active_university_students * (party2==1),coef_number_of_active_university_students_adult_party
+,Number of Active Non-workers /Adult Party,num_travel_active_non_workers * (party1==1) + num_travel_active_non_workers * (party2==1),coef_number_of_active_nonworkers_adult_party
+,Number of Active Retirees /Adult Party,num_travel_active_retirees * (party1==1) + num_travel_active_retirees * (party2==1),coef_number_of_active_retirees_adult_party
+,Number of Active Driving Age School Children /Children Party,num_travel_active_driving_age_students * (party1==1) + num_travel_active_driving_age_students * (party2==1),coef_number_of_active_driving_age_school_children_children_party
+,Number of Active Pre- Driving Age School Children /Children Party,num_travel_active_pre_driving_age_school_kids * (party1==2) + num_travel_active_pre_driving_age_school_kids * (party2==2),coef_number_of_active_pre_driving_age_school_children_children_party
+,Number of Active Part time workers /Mixed Party,num_travel_active_part_time_workers * (party1==2) + num_travel_active_part_time_workers * (party2==2),coef_number_of_active_part_time_workers_mixed_party
+,Number of Active Driving Age School Children /Mixed Party,num_travel_active_driving_age_students * (party1==3) + num_travel_active_driving_age_students * (party2==3),coef_number_of_active_driving_age_school_children_mixed_party
+,Number of Active Pre- Driving Age School Children /Mixed Party,num_travel_active_pre_driving_age_school_kids * (party1==3) + num_travel_active_pre_driving_age_school_kids * (party2==3),coef_number_of_active_pre_driving_age_school_children_mixed_party
+,Number of Active Preschool Children /Mixed Party,num_travel_active_pre_school_kids * (party1==3) + num_travel_active_pre_school_kids * (party2==3),coef_number_of_active_preschool_children_mixed_party
+,HH has no autos / Mixed Party,@(df.autos==0)*(df.party1==3)+(df.autos==0)*(df.party2==3),coef_hh_has_no_autos__mixed_party
+,HH has less autos than workers/ Mixed Party,@(df.autos<df.num_workers)*(df.party1==3)+(df.autos<df.num_workers)*(df.party2==3),coef_hh_has_less_autos_than_workers_mixed_party
+,Income more than $100k /Child Party,@(df.income>100000)*(df.party1==2)+(df.income>100000)*(df.party2==2),coef_income_more_than_100k_child_party
+,Income more than $100k /Mixed Party,@(df.income>100000)*(df.party1==3)+(df.income>100000)*(df.party2==3),coef_income_more_than_100k_mixed_party
+,Log of max window overlaps between adults,@df.log_time_window_overlap_adult*((df.party1==1)+(df.party2==1)),coef_log_of_max_window_overlaps_between_adults
+,Log of max window overlaps between children,@df.log_time_window_overlap_child*((df.party1==2)+(df.party2==2)),coef_log_of_max_window_overlaps_between_children
+,Log of max window overlaps between adult & child,@df.log_time_window_overlap_adult_child*((df.party1==3)+(df.party2==3)),coef_log_of_max_window_overlaps_between_adult_child
+,Not more than 1 travel active adult in HH,@(df.num_travel_active_adults < 2)*(((df.party1==1)+(df.party2==1))>0),coef_unavailable
+,Not more than 1 travel active child in HH,@(df.num_travel_active_children < 2)*(((df.party1==2)+(df.party2==2))>0),coef_unavailable
+,No travel-active pair adult-child in HH ,@((df.num_travel_active_adults*df.num_travel_active_children) ==0)*(((df.party1==3)+(df.party2==3))>0),coef_unavailable
+,Adjustment for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_adjustment_for_children_party_shopping_tour
+,Adjustment for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_adjustment_for_children_party_maintenance_tour
+,Adjustment for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_adjustment_for_children_party_eating_out_tour
+,Adjustment for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_adjustment_for_children_party_visiting_tour
+,Adjustment for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_adjustment_for_children_party_discretionary_tour
+,Adjustment for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_adjustment_for_mixed_party_shopping_tour
+,Adjustment for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_adjustment_for_mixed_party_maintenance_tour
+,Adjustment for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_adjustment_for_mixed_party_eating_out_tour
+,Adjustment for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_adjustment_for_mixed_party_visiting_tour
+,Adjustment for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_adjustment_for_mixed_party_discretionary_tour
+,Adjustment for shopping tour,shopping,coef_adjustment_for_shopping_tour
+,Adjustment for Maintenance tour,othmaint,coef_adjustment_for_maintenance_tour
+,Adjustment for eating out tour,eatout,coef_adjustment_for_eating_out_tour
+,Adjustment for visiting tour,social,coef_adjustment_for_visiting_tour
+,Adjustment for discretionary tour,othdiscr,coef_adjustment_for_discretionary_tour
+,Adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_adjustment_for_share_of_2_joint_tours
+,TM2 Adjustment for Children Party,@(df.party1==2)+(df.party2==2),coef_tm2_adjustment_for_children_party
+,TM2 Adjustment for Mixed Party,@(df.party1==3)+(df.party2==3),coef_tm2_adjustment_for_mixed_party
+,TM2 adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_tm2_adjustment_for_share_of_2_joint_tours
diff --git a/test/joint_tours/configs/joint_tour_frequency_composition.yaml b/test/joint_tours/configs/joint_tour_frequency_composition.yaml
new file mode 100644
index 000000000..ce31ea8df
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_frequency_composition.yaml
@@ -0,0 +1,35 @@
+LOGIT_TYPE: MNL
+
+SPEC: joint_tour_frequency_composition.csv
+COEFFICIENTS: joint_tour_frequency_composition_coeffs.csv
+
+preprocessor:
+  SPEC: joint_tour_frequency_composition_annotate_households_preprocessor.csv
+  DF: households
+  TABLES:
+    - persons
+
+ALTS_PREPROCESSOR:
+  SPEC: joint_tour_frequency_composition_annotate_alt_preprocessor.csv
+  DF: alt_tdd
+
+# define the structure of alternative table
+ALTS_TABLE_STRUCTURE:
+  PURPOSE:
+    COLUMNS:
+      - purpose1
+      - purpose2
+    VALUE_MAP:
+      5: shopping
+      6: othmaint
+      7: eatout
+      8: social
+      9: othdiscr
+  COMPOSITION:
+    COLUMNS:
+      - party1
+      - party2
+    VALUE_MAP:
+      1: adults
+      2: children
+      3: mixed
\ No newline at end of file
diff --git a/test/joint_tours/configs/joint_tour_frequency_composition_alternatives.csv b/test/joint_tours/configs/joint_tour_frequency_composition_alternatives.csv
new file mode 100644
index 000000000..657f98741
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_frequency_composition_alternatives.csv
@@ -0,0 +1,151 @@
+alt,purpose1,purpose2,party1,party2
+1,5,0,1,0
+2,5,0,2,0
+3,5,0,3,0
+4,6,0,1,0
+5,6,0,2,0
+6,6,0,3,0
+7,7,0,1,0
+8,7,0,2,0
+9,7,0,3,0
+10,8,0,1,0
+11,8,0,2,0
+12,8,0,3,0
+13,9,0,1,0
+14,9,0,2,0
+15,9,0,3,0
+16,5,5,1,1
+17,5,5,1,2
+18,5,5,1,3
+19,5,5,2,1
+20,5,5,2,2
+21,5,5,2,3
+22,5,5,3,1
+23,5,5,3,2
+24,5,5,3,3
+25,5,6,1,1
+26,5,6,1,2
+27,5,6,1,3
+28,5,6,2,1
+29,5,6,2,2
+30,5,6,2,3
+31,5,6,3,1
+32,5,6,3,2
+33,5,6,3,3
+34,5,7,1,1
+35,5,7,1,2
+36,5,7,1,3
+37,5,7,2,1
+38,5,7,2,2
+39,5,7,2,3
+40,5,7,3,1
+41,5,7,3,2
+42,5,7,3,3
+43,5,8,1,1
+44,5,8,1,2
+45,5,8,1,3
+46,5,8,2,1
+47,5,8,2,2
+48,5,8,2,3
+49,5,8,3,1
+50,5,8,3,2
+51,5,8,3,3
+52,5,9,1,1
+53,5,9,1,2
+54,5,9,1,3
+55,5,9,2,1
+56,5,9,2,2
+57,5,9,2,3
+58,5,9,3,1
+59,5,9,3,2
+60,5,9,3,3
+61,6,6,1,1
+62,6,6,1,2
+63,6,6,1,3
+64,6,6,2,1
+65,6,6,2,2
+66,6,6,2,3
+67,6,6,3,1
+68,6,6,3,2
+69,6,6,3,3
+70,6,7,1,1
+71,6,7,1,2
+72,6,7,1,3
+73,6,7,2,1
+74,6,7,2,2
+75,6,7,2,3
+76,6,7,3,1
+77,6,7,3,2
+78,6,7,3,3
+79,6,8,1,1
+80,6,8,1,2
+81,6,8,1,3
+82,6,8,2,1
+83,6,8,2,2
+84,6,8,2,3
+85,6,8,3,1
+86,6,8,3,2
+87,6,8,3,3
+88,6,9,1,1
+89,6,9,1,2
+90,6,9,1,3
+91,6,9,2,1
+92,6,9,2,2
+93,6,9,2,3
+94,6,9,3,1
+95,6,9,3,2
+96,6,9,3,3
+97,7,7,1,1
+98,7,7,1,2
+99,7,7,1,3
+100,7,7,2,1
+101,7,7,2,2
+102,7,7,2,3
+103,7,7,3,1
+104,7,7,3,2
+105,7,7,3,3
+106,7,8,1,1
+107,7,8,1,2
+108,7,8,1,3
+109,7,8,2,1
+110,7,8,2,2
+111,7,8,2,3
+112,7,8,3,1
+113,7,8,3,2
+114,7,8,3,3
+115,7,9,1,1
+116,7,9,1,2
+117,7,9,1,3
+118,7,9,2,1
+119,7,9,2,2
+120,7,9,2,3
+121,7,9,3,1
+122,7,9,3,2
+123,7,9,3,3
+124,8,8,1,1
+125,8,8,1,2
+126,8,8,1,3
+127,8,8,2,1
+128,8,8,2,2
+129,8,8,2,3
+130,8,8,3,1
+131,8,8,3,2
+132,8,8,3,3
+133,8,9,1,1
+134,8,9,1,2
+135,8,9,1,3
+136,8,9,2,1
+137,8,9,2,2
+138,8,9,2,3
+139,8,9,3,1
+140,8,9,3,2
+141,8,9,3,3
+142,9,9,1,1
+143,9,9,1,2
+144,9,9,1,3
+145,9,9,2,1
+146,9,9,2,2
+147,9,9,2,3
+148,9,9,3,1
+149,9,9,3,2
+150,9,9,3,3
diff --git a/test/joint_tours/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv b/test/joint_tours/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv
new file mode 100644
index 000000000..389d28a13
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv
@@ -0,0 +1,7 @@
+Description,Target,Expression
+,shopping,"np.where(alt_tdd.purpose1 == 5, 1, 0) + np.where(alt_tdd.purpose2 == 5, 1, 0)"
+,othmaint,"np.where(alt_tdd.purpose1 == 6, 1, 0) + np.where(alt_tdd.purpose2 == 6, 1, 0)"
+,eatout,"np.where(alt_tdd.purpose1 == 7, 1, 0) + np.where(alt_tdd.purpose2 == 7, 1, 0)"
+,social,"np.where(alt_tdd.purpose1 == 8, 1, 0) + np.where(alt_tdd.purpose2 == 8, 1, 0)"
+,othdiscr,"np.where(alt_tdd.purpose1 == 9, 1, 0) + np.where(alt_tdd.purpose2 == 9, 1, 0)"
+,num_joint_tours,shopping+othmaint+eatout+social+othdiscr
\ No newline at end of file
diff --git a/test/joint_tours/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv b/test/joint_tours/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv
new file mode 100644
index 000000000..1b4a5c91d
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv
@@ -0,0 +1,8 @@
+Description,Target,Expression
+,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)"
+,time_window_overlap_adult,_HH_OVERLAPS['aa']
+,time_window_overlap_child,_HH_OVERLAPS['cc']
+,time_window_overlap_adult_child,_HH_OVERLAPS['ac']
+logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult)
+logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child)
+logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child)
diff --git a/test/joint_tours/configs/joint_tour_frequency_composition_coeffs.csv b/test/joint_tours/configs/joint_tour_frequency_composition_coeffs.csv
new file mode 100644
index 000000000..da82e8fa9
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_frequency_composition_coeffs.csv
@@ -0,0 +1,97 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_constant_for_shopping_tour,0,T
+coef_constant_for_maintenance_tour,-1.477,F
+coef_constant_for_eating_out_tour,0.5796,F
+coef_constant_for_visiting_tour,-1.0037,F
+coef_constant_for_discretionary_tour,-1.1195,F
+coef_constant_for_2_shopping_tour,-13.70946409,F
+coef_1_shopping_and_1_maintenance_tour,-12.13684801,F
+coef_1_shopping_and_1_eating_out_tour,-12.79892713,F
+coef_1_shopping_and_1_visiting_tour,-12.22077221,F
+coef_1_shopping_and_1_discretionary_tour,-12.57867869,F
+coef_constant_for_2_maintenance_tour,-13.43572169,F
+coef_1_maintenance_and_1_eating_out_tour,-12.18915033,F
+coef_1_maintenance_and_1_visiting_tour,-11.81267905,F
+coef_1_maintenance_and_1_discretionary_tour,-12.28867197,F
+coef_constant_for_2_eating_out_tour,-13.15388273,F
+coef_1_eating_out_and_1_visiting_tour,-13.15388273,F
+coef_1_eating_out_and_1_discretionary_tour,-12.56102569,F
+coef_constant_for_2_visiting_tour,-13.15388273,F
+coef_1_visiting_and_1_discretionary_tour,-12.37222202,F
+coef_constant_for_2_discretionary_tour,-13.23532342,F
+coef_number_of_active_full_time_workers_shopping,0.09864381,F
+coef_number_of_active_nonworkers_shopping,0.393630718,F
+coef_number_of_active_pre_driving_age_school_children_shopping,-0.313021042,F
+coef_number_of_active_preschool_children_shopping,-1.213950718,F
+coef_number_of_active_nonworkers_maintenance,0.322738327,F
+coef_number_of_active_retirees_maintenance,0.298632515,F
+coef_number_of_active_driving_age_school_children_maintenance,0.503901856,F
+coef_number_of_active_preschool_children_maintenance,-1.160523204,F
+coef_number_of_active_full_time_workers_eating_out,-0.305934663,F
+coef_number_of_active_university_students_eating_out,-0.65706029,F
+coef_number_of_active_retirees_eating_out,-0.391710731,F
+coef_number_of_active_pre_driving_age_school_children_eating_out,-0.25140082,F
+coef_number_of_active_preschool_children_eating_out,-1.700731169,F
+coef_number_of_active_pre_driving_age_school_children_visiting,0.162335324,F
+coef_number_of_active_preschool_children_visiting,-0.96955678,F
+coef_number_of_active_part_time_workers_discretionary,0.217898126,F
+coef_number_of_active_university_students_discretionary,-0.610578234,F
+coef_number_of_active_driving_age_school_children_discretionary,0.359485499,F
+coef_number_of_active_preschool_children_discretionary,-1.244458661,F
+coef_hh_has_more_autos_than_workers_maintenance,-0.336188392,F
+coef_income_gtr_100k_maintenance,-0.475730683,F
+coef_income_less_than_29999__eating_out,-1.282190776,F
+coef_income_between_30000_to_59999__eating_out,-0.275046208,F
+coef_income_less_than_29999__discretionary,-0.352579013,F
+coef_income_between_30000_to_59999__discretionary,-0.191735343,F
+coef_shopping_hov_accessibility_for_2_tours,0.039513822,F
+coef_maintenance_hov_accessibility,0.128132691,F
+coef_discretionary_hov_accessibility,0.089590553,F
+coef_constant_for_children_party_shopping_tour,-5.374907972,F
+coef_constant_for_children_party_maintenance_tour,-5.144798184,F
+coef_constant_for_children_party_eating_out_tour,-4.09806907,F
+coef_constant_for_children_party_visiting_tour,-4.09806907,F
+coef_constant_for_children_party_discretionary_tour,-4.09806907,F
+coef_constant_for_mixed_party_shopping_tour,0.575879495,F
+coef_constant_for_mixed_party_maintenance_tour,0.515873866,F
+coef_constant_for_mixed_party_eating_out_tour,0.168592084,F
+coef_constant_for_mixed_party_visiting_tour,0.078060666,F
+coef_constant_for_mixed_party_discretionary_tour,0.856042068,F
+coef_number_of_active_full_time_workers_adult_party,0.599335502,F
+coef_number_of_active_part_time_workers_adult_party,1.113944272,F
+coef_number_of_active_university_students_adult_party,0.231138167,F
+coef_number_of_active_nonworkers_adult_party,0.341446999,F
+coef_number_of_active_retirees_adult_party,0.657220801,F
+coef_number_of_active_driving_age_school_children_children_party,0.580109231,F
+coef_number_of_active_pre_driving_age_school_children_children_party,0.580109231,F
+coef_number_of_active_part_time_workers_mixed_party,0.522327137,F
+coef_number_of_active_driving_age_school_children_mixed_party,0.216908669,F
+coef_number_of_active_pre_driving_age_school_children_mixed_party,0.31440104,F
+coef_number_of_active_preschool_children_mixed_party,0.897670235,F
+coef_hh_has_no_autos__mixed_party,-2.920728233,F
+coef_hh_has_less_autos_than_workers_mixed_party,-0.546339245,F
+coef_income_more_than_100k_child_party,-1.189112151,F
+coef_income_more_than_100k_mixed_party,-0.303217156,F
+coef_log_of_max_window_overlaps_between_adults,2.96902119,F
+coef_log_of_max_window_overlaps_between_children,4.673601828,F
+coef_log_of_max_window_overlaps_between_adult_child,3.523795377,F
+coef_adjustment_for_children_party_shopping_tour,0.407745781,F
+coef_adjustment_for_children_party_maintenance_tour,0.284925252,F
+coef_adjustment_for_children_party_eating_out_tour,0,T
+coef_adjustment_for_children_party_visiting_tour,0.966264444,F
+coef_adjustment_for_children_party_discretionary_tour,0.144740532,F
+coef_adjustment_for_mixed_party_shopping_tour,0.681178558,F
+coef_adjustment_for_mixed_party_maintenance_tour,0.476324741,F
+coef_adjustment_for_mixed_party_eating_out_tour,0.827297043,F
+coef_adjustment_for_mixed_party_visiting_tour,0.691826384,F
+coef_adjustment_for_mixed_party_discretionary_tour,0.697808415,F
+coef_adjustment_for_shopping_tour,0,T
+coef_adjustment_for_maintenance_tour,0.06575155,F
+coef_adjustment_for_eating_out_tour,0.0643679,F
+coef_adjustment_for_visiting_tour,0.00785518,F
+coef_adjustment_for_discretionary_tour,0.076075677,F
+coef_adjustment_for_share_of_2_joint_tours,-1.214059893,F
+coef_tm2_adjustment_for_children_party,0,F
+coef_tm2_adjustment_for_mixed_party,0,F
+coef_tm2_adjustment_for_share_of_2_joint_tours,-0.5,F
diff --git a/test/joint_tours/configs/joint_tour_participation.csv b/test/joint_tours/configs/joint_tour_participation.csv
new file mode 100644
index 000000000..9f3151129
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_participation.csv
@@ -0,0 +1,67 @@
+Label,Description,Expression,participate,not_participate
+,Dummy for full-time worker in adult party,(ptype==1) & (composition=='adults'),coef_full_time_worker_adults_only_party,
+,Dummy for part-time worker in adult party,(ptype==2) & (composition=='adults'),coef_part_time_worker_adults_only_party,
+,Dummy for university student in adult party,(ptype==3) & (composition=='adults'),coef_university_students_adults_only_party,
+,Dummy for nonworker in adult party,(ptype==4) & (composition=='adults'),coef_non_worker_adults_only_party,
+,Dummy for retiree in adult party,(ptype==5) & (composition=='adults'),coef_retiree_adults_only_party,
+,Dummy for driving school child in children party,(ptype==6) & (composition=='children'),coef_driving_age_student_children_only_party,
+,Dummy for pre-driving school child in children party,(ptype==7) & (composition=='children'),coef_pre_driving_age_student_children_only_party,
+,Dummy for preschool child in children party,(ptype==8) & (composition=='children'),coef_child_too_young_for_school_children_only_party,
+util_full_time_worker_mixed_party,Dummy for full-time worker in mixed party,(ptype==1) & (composition=='mixed'),coef_full_time_worker_mixed_party,
+util_part_time_worker_mixed_party,Dummy for part-time worker in mixed party,(ptype==2) & (composition=='mixed'),coef_part_time_worker_mixed_party,
+util_university_student_mixed_party,Dummy for university student in mixed party,(ptype==3) & (composition=='mixed'),coef_university_student_mixed_party,
+util_non_worker_mixed_party,Dummy for nonworker in mixed party,(ptype==4) & (composition=='mixed'),coef_non_worker_mixed_party,
+util_retiree_mixed_party,Dummy for retiree in mixed party,(ptype==5) & (composition=='mixed'),coef_retiree_mixed_party,
+util_child_too_young_for_school_mixed_party,Dummy for driving school child in mixed party,(ptype==6) & (composition=='mixed'),coef_child_too_young_for_school_mixed_party,
+util_pre_driving_age_student_mixed_party,Dummy for pre-driving school child in mixed party,(ptype==7) & (composition=='mixed'),coef_pre_driving_age_student_mixed_party,
+util_driving_age_student_mixed_party,Dummy for preschool child in mixed party,(ptype==8) & (composition=='mixed'),coef_driving_age_student_mixed_party,
+#,,,,
+,Dummy for part-time worker on maintenance joint tour ,(ptype==2) & (tour_type=='othmaint'),coef_part_time_worker_specific_to_maintenance_joint_tours,
+,Dummy for non-worker on maintenance joint tour ,(ptype==4) & (tour_type=='othmaint'),coef_non_worker_specific_to_maintenance_joint_tours,
+,Dummy for preschool child on maintenance joint tour ,(ptype==8) & (tour_type=='othmaint'),coef_pre_school_kid_specific_to_maintenance_joint_tours,
+,Dummy for full-time worker on eating out joint tour ,(ptype==1) & (tour_type=='eatout'),coef_full_time_worker_specific_to_eating_out_joint_tours,
+,Dummy for part-time worker on eating out joint tour ,(ptype==2) & (tour_type=='eatout'),coef_part_time_worker_specific_to_eating_out_joint_tours,
+,Dummy for pre-driving age school child on eating out joint tour ,(ptype==7) & (tour_type=='eatout'),coef_pre_driving_age_student_specific_to_eating_out_joint_tours,
+,Dummy for part-time worker on discretionary joint tour ,(ptype==2) & (tour_type=='othdiscr'),coef_part_time_worker_specific_to_discretionary_joint_tours,
+,Dummy for retiree on discretionary joint tour ,(ptype==5) & (tour_type=='othdiscr'),coef_retiree_specific_to_discretionary_joint_tours,
+,Dummy for driving school child on discretionary joint tour ,(ptype==6) & (tour_type=='othdiscr'),coef_driving_age_student_specific_to_discretionary_joint_tours,
+,Dummy for pre-driving school child on discretionary joint tour ,(ptype==7) & (tour_type=='othdiscr'),coef_pre_driving_age_student_specific_to_discretionary_joint_tours,
+,Dummy for part-time worker on visiting joint tour ,(ptype==2) & (tour_type=='social'),coef_part_time_worker_specific_to_visiting_joint_tours,
+,Dummy for non-worker on visiting joint tour ,(ptype==4) & (tour_type=='social'),coef_non_worker_specific_to_visiting_joint_tours,
+,Dummy for retiree on visiting joint tour ,(ptype==5) & (tour_type=='social'),coef_retiree_specific_to_visiting_joint_tours,
+,Dummy for driving school child on visiting joint tour ,(ptype==6) & (tour_type=='social'),coef_driving_age_student_specific_to_visiting_joint_tours,
+,Dummy for preschool child on visiting joint tour ,(ptype==8) & (tour_type=='social'),coef_pre_school_kid_specific_to_visiting_joint_tours,
+#,,,,
+,Dummy for low car ownership for adult in adult party,(autos<num_workers)&(composition=='adults')&adult,coef_adult_fewer_automobiles_than_workers_adult_only_party,
+,Dummy for high car ownership for adult in mixed party,(autos>num_workers)&(composition=='mixed')&adult,coef_adult_more_automobiles_than_workers_mixed_party,
+,Dummy for no car for child in mixed party,(autos==0)&(composition=='mixed')&child,coef_child_zero_automobiles_mixed_party,
+#,,,,
+,Dummy for low income for adult in adult party,(income<30000)&(composition=='adults')&adult,coef_dummy_for_low_income_for_adult_in_adult_party,
+,Dummy for high income for adult in mixed party,(income>100000)&(composition=='mixed')&adult,coef_dummy_for_high_income_for_adult_in_mixed_party,
+,Dummy for high income for child in mixed party,(income>100000)&(composition=='mixed')&child,coef_dummy_for_high_income_for_child_in_mixed_party,
+#,,,,
+,No of HH joint tours for adult in adult party,(adult&(composition=='adults'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only,
+,No of HH joint tours for adult in mixed party,(adult&(composition=='mixed'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed,
+,No of HH joint tours for child in children party,(child&(composition=='children'))*num_hh_joint_tours,coef_child_number_of_joint_tours_child_only,
+,No of HH joint tours for child in mixed party,(child&(composition=='mixed'))*num_hh_joint_tours,coef_child_number_of_joint_tours_mixed,
+#,,,,
+,No of other HH adults for adult in adult party,(adult & (composition=='adults')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party,
+,No of other HH adults for adult in mixed party,(adult & (composition=='mixed')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party,
+,No of other HH children for child in children party,(~adult & (composition=='children')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_child_only_party,
+,No of other HH children for child in mixed party,(~adult & (composition=='mixed')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_mixed,
+#,,,,
+util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & (composition=='adults')) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,
+util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & (composition=='mixed')) * log_time_window_overlap_adult_child,coef_adult_log_of_max_window_overlap_with_a_child_mixed,
+util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult &(composition=='mixed')) * log_time_window_overlap_adult_child,coef_child_log_of_max_window_overlap_with_an_adult_mixed,
+util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & (composition=='children')) * log_time_window_overlap_child,coef_child_log_of_max_window_overlap_with_a_child_child,
+#,,,,
+util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & (composition=='children'),coef_unavailable,
+util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & (composition=='adults'),coef_unavailable,
+util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable,
+#,,,,
+util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & (composition=='adults') & (num_travel_active_adults<3),,coef_unavailable
+util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & (composition=='mixed') & (num_travel_active_adults<2),,coef_unavailable
+util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & (composition=='children') & (num_travel_active_children<3),,coef_unavailable
+util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & (composition == 'mixed') & (num_travel_active_children<2),,coef_unavailable
+#,,,,
+,Global adjustment constant for whether person participated or not,1,coef_global_adj_for_paticipation,
diff --git a/test/joint_tours/configs/joint_tour_participation.yaml b/test/joint_tours/configs/joint_tour_participation.yaml
new file mode 100644
index 000000000..6e4fe5050
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_participation.yaml
@@ -0,0 +1,21 @@
+
+SPEC: joint_tour_participation.csv
+COEFFICIENTS: joint_tour_participation_coefficients.csv
+
+LOGIT_TYPE: MNL
+
+max_participation_choice_iterations: 1000
+
+preprocessor:
+  SPEC: joint_tour_participation_annotate_participants_preprocessor
+  DF: participants
+  TABLES:
+    - tours
+#    - persons
+#    - accessibility
+
+annotate_persons:
+  SPEC: annotate_persons_jtp
+  DF: persons
+  TABLES:
+    - joint_tour_participants
diff --git a/test/joint_tours/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/test/joint_tours/configs/joint_tour_participation_annotate_participants_preprocessor.csv
new file mode 100644
index 000000000..95dac2f9e
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_participation_annotate_participants_preprocessor.csv
@@ -0,0 +1,13 @@
+Description,Target,Expression
+,_P_OVERLAPS,person_time_window_overlap(persons)
+,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)"
+,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)"
+,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)"
+logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult)
+logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child)
+logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child)
+#,,
+,_JOINT_TOURS,tours[tours.tour_category=='joint']
+,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), participants.household_id)"
+#,,
+,person_is_preschool,participants.ptype == 8
\ No newline at end of file
diff --git a/test/joint_tours/configs/joint_tour_participation_coefficients.csv b/test/joint_tours/configs/joint_tour_participation_coefficients.csv
new file mode 100644
index 000000000..8126d3088
--- /dev/null
+++ b/test/joint_tours/configs/joint_tour_participation_coefficients.csv
@@ -0,0 +1,59 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_full_time_worker_adults_only_party,-0.844885174,F
+coef_part_time_worker_adults_only_party,-1.837563723,F
+coef_university_students_adults_only_party,-0.970251833,F
+coef_non_worker_adults_only_party,-0.757939204,F
+coef_retiree_adults_only_party,1.196798335,F
+coef_driving_age_student_children_only_party,-12.08924396,F
+coef_pre_driving_age_student_children_only_party,-16.16999328,F
+coef_child_too_young_for_school_children_only_party,-16.16999328,F
+coef_full_time_worker_mixed_party,0.453048547,F
+coef_part_time_worker_mixed_party,1.263225489,F
+coef_university_student_mixed_party,1.561741231,F
+coef_non_worker_mixed_party,2.90040237,F
+coef_retiree_mixed_party,1.043276837,F
+coef_child_too_young_for_school_mixed_party,-1.916267403,F
+coef_pre_driving_age_student_mixed_party,-1.91645719,F
+coef_driving_age_student_mixed_party,-0.933863842,F
+#,,
+coef_part_time_worker_specific_to_maintenance_joint_tours,0.766072366,F
+coef_non_worker_specific_to_maintenance_joint_tours,0.971450303,F
+coef_pre_school_kid_specific_to_maintenance_joint_tours,-0.52817951,F
+coef_full_time_worker_specific_to_eating_out_joint_tours,0.535579735,F
+coef_part_time_worker_specific_to_eating_out_joint_tours,1.23257663,F
+coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.535608884,F
+coef_part_time_worker_specific_to_discretionary_joint_tours,0.539337302,F
+coef_retiree_specific_to_discretionary_joint_tours,1.105118702,F
+coef_driving_age_student_specific_to_discretionary_joint_tours,-1.151066894,F
+coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.798684189,F
+coef_part_time_worker_specific_to_visiting_joint_tours,1.075535443,F
+coef_non_worker_specific_to_visiting_joint_tours,1.075535443,F
+coef_retiree_specific_to_visiting_joint_tours,-1.930328716,F
+coef_driving_age_student_specific_to_visiting_joint_tours,-1.334515344,F
+coef_pre_school_kid_specific_to_visiting_joint_tours,1.552698221,F
+#,,
+coef_adult_fewer_automobiles_than_workers_adult_only_party,1.292641814,F
+coef_adult_more_automobiles_than_workers_mixed_party,-0.390671553,F
+coef_child_zero_automobiles_mixed_party,-1.546687521,F
+#,,
+coef_dummy_for_low_income_for_adult_in_adult_party,-0.680688378,F
+coef_dummy_for_high_income_for_adult_in_mixed_party,-0.20257569,F
+coef_dummy_for_high_income_for_child_in_mixed_party,-0.74160668,F
+#,,
+coef_adult_number_of_joint_tours_adult_only,-0.599443484,F
+coef_adult_number_of_joint_tours_mixed,-0.218541079,F
+coef_child_number_of_joint_tours_child_only,-0.313832442,F
+coef_child_number_of_joint_tours_mixed,-0.241748337,F
+#,,
+coef_adult_number_of_other_adults_in_the_household_adults_only_party,-0.747528682,F
+coef_adult_number_of_other_adults_in_the_household_mixed_party,-0.285988025,F
+coef_child_number_of_other_children_in_the_household_child_only_party,-2.305908575,F
+coef_child_number_of_other_children_in_the_household_mixed,-0.471545203,F
+#,,
+coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,1.634378325,F
+coef_adult_log_of_max_window_overlap_with_a_child_mixed,0.057012356,F
+coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.616796615,F
+coef_child_log_of_max_window_overlap_with_a_child_child,10.70256432,F
+#,,
+coef_global_adj_for_paticipation,-1,T
diff --git a/test/joint_tours/configs/network_los.yaml b/test/joint_tours/configs/network_los.yaml
new file mode 100644
index 000000000..391125a38
--- /dev/null
+++ b/test/joint_tours/configs/network_los.yaml
@@ -0,0 +1,14 @@
+# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
+read_skim_cache: False
+# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
+write_skim_cache: True
+
+zone_system: 1
+
+taz_skims: skims.omx
+
+skim_time_periods:
+    time_window: 1440
+    period_minutes: 60
+    periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59
+    labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV']
\ No newline at end of file
diff --git a/test/joint_tours/configs/settings.yaml b/test/joint_tours/configs/settings.yaml
new file mode 100644
index 000000000..64584bdf4
--- /dev/null
+++ b/test/joint_tours/configs/settings.yaml
@@ -0,0 +1,79 @@
+# input tables
+input_table_list:
+  - tablename: households
+    filename: households.csv
+    index_col: household_id
+    rename_columns:
+      unique_hh_id: household_id
+      NP: hhsize
+      hh_workers_from_esr: num_workers
+      VEH: auto_ownership
+      MAZ: home_zone_id
+      HINCP: income
+    keep_columns:
+      - home_zone_id
+      - income
+      - hhsize
+      - HHT
+      - auto_ownership
+      - num_workers
+  - tablename: persons
+    filename: persons.csv
+    index_col: person_id
+    rename_columns:
+      unique_hh_id: household_id
+      AGEP: age
+      SPORDER: PNUM
+      SEX: sex
+      employed: pemploy
+      student_status: pstudent
+      person_type: ptype
+    keep_columns:
+      - household_id
+      - age
+      - PNUM
+      - sex
+      - pemploy
+      - pstudent
+      - ptype
+  - tablename: land_use
+    filename: land_use.csv
+    index_col: zone_id
+    rename_columns:
+      MAZ_ORIGINAL: zone_id
+      CountyID: county_id
+      TAZ_ORIGINAL: TAZ
+      DistID: DISTRICT
+      HH: TOTHH
+      POP: TOTPOP
+      ACRES: TOTACRE
+      emp_total: TOTEMP
+    keep_columns:
+      - TAZ
+      - DISTRICT
+      - SD
+      - county_id
+      - TOTHH
+      - TOTPOP
+      - TOTACRE
+  - tablename: tours
+    filename: mandatory_tours.csv
+    # since tours has a canonical index name 'tour_id', we must explicitly indicate that no index should be assigned
+    # canonical index_col 'tour_id' will be assigned by initialize_tours
+    index_col:
+    rename_columns:
+      hh_id: household_id
+      start_period: start
+      end_period: end
+    keep_columns:
+      - person_id
+      - household_id
+      - person_type
+      - tour_category
+      - tour_purpose
+      - orig_mgra
+      - dest_mgra
+      - start
+      - end
+
+trace_hh_id: 
\ No newline at end of file
diff --git a/test/joint_tours/configs/tour_departure_and_duration_alternatives.csv b/test/joint_tours/configs/tour_departure_and_duration_alternatives.csv
new file mode 100644
index 000000000..bddab06b9
--- /dev/null
+++ b/test/joint_tours/configs/tour_departure_and_duration_alternatives.csv
@@ -0,0 +1,191 @@
+start,end
+5,5
+5,6
+5,7
+5,8
+5,9
+5,10
+5,11
+5,12
+5,13
+5,14
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+5,17
+5,18
+5,19
+5,20
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+5,23
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+6,14
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+6,22
+6,23
+7,7
+7,8
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+7,10
+7,11
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+7,13
+7,14
+7,15
+7,16
+7,17
+7,18
+7,19
+7,20
+7,21
+7,22
+7,23
+8,8
+8,9
+8,10
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+8,12
+8,13
+8,14
+8,15
+8,16
+8,17
+8,18
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+8,21
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+8,23
+9,9
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+9,12
+9,13
+9,14
+9,15
+9,16
+9,17
+9,18
+9,19
+9,20
+9,21
+9,22
+9,23
+10,10
+10,11
+10,12
+10,13
+10,14
+10,15
+10,16
+10,17
+10,18
+10,19
+10,20
+10,21
+10,22
+10,23
+11,11
+11,12
+11,13
+11,14
+11,15
+11,16
+11,17
+11,18
+11,19
+11,20
+11,21
+11,22
+11,23
+12,12
+12,13
+12,14
+12,15
+12,16
+12,17
+12,18
+12,19
+12,20
+12,21
+12,22
+12,23
+13,13
+13,14
+13,15
+13,16
+13,17
+13,18
+13,19
+13,20
+13,21
+13,22
+13,23
+14,14
+14,15
+14,16
+14,17
+14,18
+14,19
+14,20
+14,21
+14,22
+14,23
+15,15
+15,16
+15,17
+15,18
+15,19
+15,20
+15,21
+15,22
+15,23
+16,16
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+17,18
+17,19
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+17,21
+17,22
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+18,18
+18,19
+18,20
+18,21
+18,22
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+19,19
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+19,23
+20,20
+20,21
+20,22
+20,23
+21,21
+21,22
+21,23
+22,22
+22,23
+23,23
\ No newline at end of file
diff --git a/test/joint_tours/test_joint_tours.py b/test/joint_tours/test_joint_tours.py
new file mode 100644
index 000000000..2edccb042
--- /dev/null
+++ b/test/joint_tours/test_joint_tours.py
@@ -0,0 +1,265 @@
+import logging
+import pytest
+import os
+import shutil
+import pandas as pd
+import numpy as np
+from numpy import dot
+from numpy.linalg import norm
+
+# import models is necessary to initalize the model steps with orca
+from activitysim.abm import models
+from activitysim.core import pipeline, config
+from activitysim.core import tracing
+
+logger = logging.getLogger(__name__)
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def module() -> str:
+    """
+    A pytest fixture that returns the data folder location.
+    :return: folder location for any necessary data to initialize the tests
+    """
+    return "joint_tours"
+
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+# def tables() -> dict[str, str]:
+def tables(prepare_module_inputs) -> dict[str, str]:
+    """
+    A pytest fixture that returns the "mock" tables to build pipeline dataframes. The
+    key-value pair is the name of the table and the index column.
+    :return: dict
+    """
+    return {
+        "land_use": "MAZ_ORIGINAL",
+        "persons": "person_id",
+        "households": "household_id",
+        "accessibility": "MAZ_ORIGINAL",
+        "tours": "tour_id",
+    }
+
+
+# Used by conftest.py initialize_pipeline method
+# Set to true if you need to read skims into the pipeline
+@pytest.fixture(scope="module")
+def initialize_network_los() -> bool:
+    """
+    A pytest boolean fixture indicating whether network skims should be read from the
+    fixtures test data folder.
+    :return: bool
+    """
+    return False
+
+
+# Used by conftest.py reconnect_pipeline method
+@pytest.fixture(scope="module")
+def load_checkpoint() -> bool:
+    """
+    checkpoint to be loaded from the pipeline when reconnecting.
+    """
+    return "initialize_households"
+
+
+@pytest.mark.skipif(
+    os.path.isfile("test/joint_tours/output/pipeline.h5"),
+    reason="no need to recreate pipeline store if already exist",
+)
+def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog):
+    # Run summarize model
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(models=["initialize_landuse", "initialize_households"])
+    person_df = pipeline.get_table("persons")
+    pipeline.close_pipeline()
+
+
+def test_joint_tours_frequency_composition(
+    reconnect_pipeline: pipeline.Pipeline, caplog
+):
+
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(
+        models=["joint_tour_frequency_composition"],
+        resume_after="initialize_households",
+    )
+
+    pipeline.close_pipeline()
+
+
+def test_joint_tours_participation(reconnect_pipeline: pipeline.Pipeline, caplog):
+
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(
+        models=["joint_tour_participation"],
+        resume_after="joint_tour_frequency_composition",
+    )
+
+    pipeline.close_pipeline()
+
+
+# fetch/prepare existing files for model inputs
+# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim
+@pytest.fixture(scope="module")
+def prepare_module_inputs() -> None:
+    """
+    copy input files from sharepoint into test folder
+
+    create unique person id in person file
+
+    :return: None
+    """
+    # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/
+    # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1
+    test_dir = os.path.join("test", "joint_tours", "data")
+
+    accessibility_file = os.path.join(test_dir, "tm2_outputs", "accessibilities.csv")
+    household_file = os.path.join(test_dir, "popsyn", "households.csv")
+    person_file = os.path.join(test_dir, "popsyn", "persons.csv")
+    landuse_file = os.path.join(test_dir, "landuse", "maz_data_withDensity.csv")
+
+    shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv"))
+    shutil.copy(household_file, os.path.join(test_dir, "households.csv"))
+    shutil.copy(person_file, os.path.join(test_dir, "persons.csv"))
+    shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv"))
+
+    # add original maz id to accessibility table
+    land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv"))
+
+    accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv"))
+
+    accessibility_df = pd.merge(
+        accessibility_df,
+        land_use_df[["MAZ", "MAZ_ORIGINAL"]].rename(columns={"MAZ": "mgra"}),
+        how="left",
+        on="mgra",
+    )
+
+    accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False)
+
+    # currently household file has to have these two columns, even before annotation
+    # because annotate person happens before household and uses these two columns
+    # TODO find a way to get around this
+    ####
+
+    # household file from populationsim
+    household_df = pd.read_csv(os.path.join(test_dir, "households.csv"))
+
+    household_columns_dict = {"HHID": "household_id", "MAZ": "home_zone_id"}
+
+    household_df.rename(columns=household_columns_dict, inplace=True)
+
+    # get columns from ctramp output
+    tm2_simulated_household_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "householdData_1.csv")
+    )
+    tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    household_df = pd.merge(
+        household_df,
+        tm2_simulated_household_df[
+            [
+                "household_id",
+                "autos",
+                "automated_vehicles",
+                "transponder",
+                "cdap_pattern",
+                "jtf_choice",
+            ]
+        ],
+        how="inner",  # tm2 is not 100% sample run
+        on="household_id",
+    )
+
+    household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False)
+
+    # person file from populationsim
+    person_df = pd.read_csv(os.path.join(test_dir, "persons.csv"))
+
+    person_columns_dict = {"HHID": "household_id", "PERID": "person_id"}
+
+    person_df.rename(columns=person_columns_dict, inplace=True)
+
+    # get columns from ctramp result
+    tm2_simulated_person_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "personData_1.csv")
+    )
+    tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    person_df = pd.merge(
+        person_df,
+        tm2_simulated_person_df[
+            [
+                "household_id",
+                "person_id",
+                "type",
+                "value_of_time",
+                "activity_pattern",
+                "imf_choice",
+                "inmf_choice",
+                "fp_choice",
+                "reimb_pct",
+                "workDCLogsum",
+                "schoolDCLogsum",
+            ]
+        ],
+        how="inner",  # ctramp might not be 100% sample run
+        on=["household_id", "person_id"],
+    )
+
+    person_df["PNUM"] = person_df.groupby("household_id")["person_id"].rank()
+
+    person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False)
+
+    ## get tour data from tm2 output
+
+    tm2_simulated_indiv_tour_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "indivTourData_1.csv")
+    )
+    tm2_simulated_indiv_tour_df = tm2_simulated_indiv_tour_df[
+        tm2_simulated_indiv_tour_df.tour_category == "MANDATORY"
+    ]
+
+    tm2_simulated_tour_df = pd.concat(
+        [tm2_simulated_indiv_tour_df], sort=False, ignore_index=True
+    )
+
+    tm2_simulated_tour_df.rename(columns={"hh_id": "household_id"}).to_csv(
+        os.path.join(test_dir, "tours.csv"), index=False
+    )
+
+
+def create_summary(input_df, key, out_col="Share") -> pd.DataFrame:
+    """
+    Create summary for the input data.
+    1. group input data by the "key" column
+    2. calculate the percent of input data records in each "key" category.
+
+    :return: pd.DataFrame
+    """
+
+    out_df = input_df.groupby(key).size().reset_index(name="Count")
+    out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4)
+
+    return out_df[[key, out_col]]
+
+
+def cosine_similarity(a, b):
+    """
+    Computes cosine similarity between two vectors.
+
+    Cosine similarity is used here as a metric to measure similarity between two sequence of numbers.
+    Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them
+    i.e., dot products of the vectors divided by the product of their lengths.
+
+    :return:
+    """
+
+    return dot(a, b) / (norm(a) * norm(b))
diff --git a/test/non_mandatory_tour_frequency/.gitkeep b/test/non_mandatory_tour_frequency/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/non_mandatory_tour_frequency/configs/NonMandatoryIndividualTourFrequency.xls b/test/non_mandatory_tour_frequency/configs/NonMandatoryIndividualTourFrequency.xls
new file mode 100644
index 000000000..4726c3f73
Binary files /dev/null and b/test/non_mandatory_tour_frequency/configs/NonMandatoryIndividualTourFrequency.xls differ
diff --git a/test/non_mandatory_tour_frequency/configs/annotate_households.csv b/test/non_mandatory_tour_frequency/configs/annotate_households.csv
new file mode 100644
index 000000000..ac21d3797
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/annotate_households.csv
@@ -0,0 +1,13 @@
+Description,Target,Expression
+#,, annotate households table after import
+,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
+number of fulltime workers,num_full_time_workers,"_PERSON_COUNT('ptype == 1', persons, households)"
+number of parttime workers,num_part_time_workers,"_PERSON_COUNT('ptype == 2', persons, households)"
+number of university student,num_university_students,"_PERSON_COUNT('ptype == 3', persons, households)"
+number of non-workers,num_non_workers,"_PERSON_COUNT('ptype == 4', persons, households)"
+number of retirees,num_retirees,"_PERSON_COUNT('ptype == 5', persons, households)"
+number of driving age students,num_driving_age_students,"_PERSON_COUNT('ptype == 6', persons, households)"
+number of pre-driving age school kids,num_pre_driving_age_school_kids,"_PERSON_COUNT('ptype == 7', persons, households)"
+number of pre-school kids,num_pre_school_kids,"_PERSON_COUNT('ptype == 8', persons, households)"
+number of pre-driving age school kids who go out,num_pre_driving_age_school_kids_go_out,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons, households)"
+number of pre-school kids who go out,num_pre_school_kids_go_out,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons, households)"
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/annotate_landuse.csv b/test/non_mandatory_tour_frequency/configs/annotate_landuse.csv
new file mode 100644
index 000000000..65237c0ad
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/annotate_landuse.csv
@@ -0,0 +1,6 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+household_density,household_density,land_use.TOTHH / land_use.TOTACRE
+employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE
+population_density,population_density,land_use.TOTPOP / land_use.TOTACRE
+density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/annotate_persons.csv b/test/non_mandatory_tour_frequency/configs/annotate_persons.csv
new file mode 100644
index 000000000..a68eb7e67
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/annotate_persons.csv
@@ -0,0 +1,17 @@
+Description,Target,Expression
+#,, annotate persons table after import
+age_16_to_19,age_16_to_19,"persons.age.between(16, 19)"
+age_16_p,age_16_p,persons.age >= 16
+adult,adult,persons.age >= 18
+male,male,persons.sex == 1
+female,female,persons.sex == 2
+,ptype,0
+,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)"
+,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)"
+,ptype,"np.where(persons.type == 'University student', 3, ptype)"
+,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)"
+,ptype,"np.where(persons.type == 'Retired', 5, ptype)"
+,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)"
+,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)"
+,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)"
+home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)"
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/annotate_persons_after_hh.csv b/test/non_mandatory_tour_frequency/configs/annotate_persons_after_hh.csv
new file mode 100644
index 000000000..0dfa16be6
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/annotate_persons_after_hh.csv
@@ -0,0 +1,5 @@
+Description,Target,Expression
+#,, annotate persons table after annotate_households
+#,, adults get full hh_value_of_time and children get 60%
+,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)"
+,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)"
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/annotate_persons_nmtf.csv b/test/non_mandatory_tour_frequency/configs/annotate_persons_nmtf.csv
new file mode 100644
index 000000000..07890f237
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/annotate_persons_nmtf.csv
@@ -0,0 +1,10 @@
+Description,Target,Expression
+#,, annotate persons table after non_mandatory_tour_frequency model has run
+num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)
+num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours
diff --git a/test/non_mandatory_tour_frequency/configs/constants.yaml b/test/non_mandatory_tour_frequency/configs/constants.yaml
new file mode 100644
index 000000000..b0bd5a1f3
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/constants.yaml
@@ -0,0 +1,68 @@
+## ActivitySim
+## See full license in LICENSE.txt.
+
+
+#HHT_NONE: 0
+#HHT_FAMILY_MARRIED: 1
+#HHT_FAMILY_MALE: 2
+#HHT_FAMILY_FEMALE: 3
+#HHT_NONFAMILY_MALE_ALONE: 4
+#HHT_NONFAMILY_MALE_NOTALONE: 5
+#HHT_NONFAMILY_FEMALE_ALONE: 6
+#HHT_NONFAMILY_FEMALE_NOTALONE: 7
+
+# convenience for expression files
+HHT_NONFAMILY: [4, 5, 6, 7]
+HHT_FAMILY: [1, 2, 3]
+
+PSTUDENT_GRADE_OR_HIGH: 1
+PSTUDENT_UNIVERSITY: 2
+PSTUDENT_NOT: 3
+
+GRADE_SCHOOL_MAX_AGE: 14
+GRADE_SCHOOL_MIN_AGE: 5
+
+SCHOOL_SEGMENT_NONE: 0
+SCHOOL_SEGMENT_GRADE: 1
+SCHOOL_SEGMENT_HIGH: 2
+SCHOOL_SEGMENT_UNIV: 3
+
+INCOME_SEGMENT_LOW: 1
+INCOME_SEGMENT_MED: 2
+INCOME_SEGMENT_HIGH: 3
+INCOME_SEGMENT_VERYHIGH: 4
+
+PEMPLOY_FULL: 1
+PEMPLOY_PART: 2
+PEMPLOY_NOT: 3
+PEMPLOY_CHILD: 4
+
+PTYPE_FULL: &ptype_full 1
+PTYPE_PART: &ptype_part 2
+PTYPE_UNIVERSITY: &ptype_university 3
+PTYPE_NONWORK: &ptype_nonwork 4
+PTYPE_RETIRED: &ptype_retired 5
+PTYPE_DRIVING: &ptype_driving 6
+PTYPE_SCHOOL: &ptype_school 7
+PTYPE_PRESCHOOL:  &ptype_preschool 8
+
+# these appear as column headers in non_mandatory_tour_frequency.csv
+PTYPE_NAME:
+  *ptype_full: PTYPE_FULL
+  *ptype_part: PTYPE_PART
+  *ptype_university: PTYPE_UNIVERSITY
+  *ptype_nonwork: PTYPE_NONWORK
+  *ptype_retired: PTYPE_RETIRED
+  *ptype_driving: PTYPE_DRIVING
+  *ptype_school: PTYPE_SCHOOL
+  *ptype_preschool: PTYPE_PRESCHOOL
+
+
+CDAP_ACTIVITY_MANDATORY: M
+CDAP_ACTIVITY_NONMANDATORY: N
+CDAP_ACTIVITY_HOME: H
+
+# Correction for transit skim expressions
+# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and
+# therefore need to be divided by the scale factor if used in expressions
+TRANSIT_SCALE_FACTOR: 100
diff --git a/test/non_mandatory_tour_frequency/configs/initialize_households.yaml b/test/non_mandatory_tour_frequency/configs/initialize_households.yaml
new file mode 100644
index 000000000..07e34e8a4
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/initialize_households.yaml
@@ -0,0 +1,48 @@
+annotate_tables:
+  - tablename: persons
+    column_map:
+      HHID: household_id
+      PERID: person_id
+      AGEP: age
+      SEX: sex
+      SCHL: education_attainment
+      OCCP: occupation
+      WKHP: hours_worked
+      WKW: weeks_worked
+      EMPLOYED: employment_status
+      ESR: esr
+      SCHG: grade_attending
+      activity_pattern: cdap_activity
+    annotate:
+      SPEC: annotate_persons
+      DF: persons
+      TABLES:
+        - households
+  - tablename: households
+    column_map:
+      HHID: household_id
+      MAZ: home_zone_id
+      HHINCADJ: income
+      NWRKRS_ESR: num_workers
+      VEH: auto_ownership
+      NP: hhsize
+      HHT: hh_type
+      BLD: building_size
+      TYPE: hh_unit_type
+      MTCCountyID: county_id
+    annotate:
+      SPEC: annotate_households
+      DF: households
+      TABLES:
+        - persons
+        - land_use
+
+CONSTANTS:
+  PTYPE_FULL: 1
+  PTYPE_PART: 2
+  PTYPE_UNIVERSITY: 3
+  PTYPE_NONWORK: 4
+  PTYPE_RETIRED: 5
+  PTYPE_DRIVING: 6
+  PTYPE_SCHOOL: 7
+  PTYPE_PRESCHOOL: 8
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/initialize_landuse.yaml b/test/non_mandatory_tour_frequency/configs/initialize_landuse.yaml
new file mode 100644
index 000000000..7cc1d7e34
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/initialize_landuse.yaml
@@ -0,0 +1,54 @@
+annotate_tables:
+  - tablename: land_use
+    column_map:
+        MAZ_ORIGINAL: zone_id
+        CountyID: county_id
+        DistID: DISTRICT
+        HH: TOTHH
+        POP: TOTPOP
+        ACRES: TOTACRE
+        emp_total: TOTEMP
+    annotate:
+      SPEC: annotate_landuse
+      DF: land_use
+  - tablename: accessibility
+    column_map:
+        column_1: nonmandatory_auto_accessibility
+        column_2: nonmandatory_transit_accessibility
+        column_3: nonmandatory_nm_accessibility
+        column_4: nonmandatory_sov0_accessibility
+        column_5: nonmandatory_sov1_accessibility
+        column_6: nonmandatory_sov2_accessibility
+        column_7: nonmandatory_hov0_accessibility
+        column_8: nonmandatory_hov1_accessibility
+        column_9: nonmandatory_hov2_accessibility
+        column_10: shop_hov_insufficient_accessibility
+        column_11: shop_hov_sufficient_accessibility
+        column_12: shop_hov_oversufficient_accessibility
+        column_13: maint_hov_insufficient_accessibility
+        column_14: maint_hov_sufficient_accessibility
+        column_15: maint_hov_oversufficient_accessibility
+        column_16: eat_hov_insufficient_accessibility
+        column_17: eat_hov_sufficient_accessibility
+        column_18: eat_hov_oversufficient_accessibility
+        column_19: visit_hov_insufficient_accessibility
+        column_20: visit_hov_sufficient_accessibility
+        column_21: visit_hov_oversufficient_accessibility
+        column_22: discr_hov_insufficient_accessibility
+        column_23: discr_hov_sufficient_accessibility
+        column_24: discr_hov_oversufficient_accessibility
+        column_25: escort_hov_insufficient_accessibility
+        column_26: escort_hov_sufficient_accessibility
+        column_27: escort_hov_oversufficient_accessibility
+        column_28: shop_sov_insufficient_accessibility
+        column_29: shop_sov_sufficient_accessibility
+        column_30: shop_sov_oversufficient_accessibility
+        column_31: maint_sov_insufficient_accessibility
+        column_32: maint_sov_sufficient_accessibility
+        column_33: maint_sov_oversufficient_accessibility
+        column_40: discr_sov_insufficient_accessibility
+        column_41: discr_sov_sufficient_accessibility
+        column_42: discr_sov_oversufficient_accessibility
+        column_45: total_emp_accessibility
+        column_47: hh_walktransit_accessibility
+        mgra : zone_id
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/network_los.yaml b/test/non_mandatory_tour_frequency/configs/network_los.yaml
new file mode 100644
index 000000000..391125a38
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/network_los.yaml
@@ -0,0 +1,14 @@
+# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
+read_skim_cache: False
+# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
+write_skim_cache: True
+
+zone_system: 1
+
+taz_skims: skims.omx
+
+skim_time_periods:
+    time_window: 1440
+    period_minutes: 60
+    periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59
+    labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV']
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.csv
new file mode 100644
index 000000000..7a3fc8703
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.csv
@@ -0,0 +1,187 @@
+Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL
+util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour
+util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour
+util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour
+util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour
+util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour
+util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour
+util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours  = 0 (No Prior Tours),(tot_tours == 0) & (num_mandatory_tours == 0) & (num_person_joint_tours == 0),coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable
+util_total_number_of_tours_is_0_prior_tours,Total Number of Tours  = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mandatory_tours > 0) | (num_person_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours
+util_total_number_of_tours_is_1,Total Number of Tours  = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,,,,,
+util_total_number_of_tours_is_2,Total Number of Tours  = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,,,,,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2
+util_total_number_of_tours_is_3,Total Number of Tours  = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,,coef_total_number_of_tours_is_3,,,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3
+util_total_number_of_tours_is_4,Total Number of Tours  = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,,coef_total_number_of_tours_is_4,,,,
+util_total_number_of_tours_is_5,Total Number of Tours  = 5,tot_tours == 5,coef_unavailable,coef_total_number_of_tours_is_5,,coef_unavailable,coef_unavailable,,,
+util_total_number_of_tours_is_6_plus,Total Number of Tours  = 6+,tot_tours > 5,coef_unavailable,coef_total_number_of_tours_is_6_plus,,coef_unavailable,coef_unavailable,,,
+util_total_number_of_tours_is_1+,Total Number of Tours  >= 1,tot_tours >= 1,,,,,,coef_total_number_of_tours_is_1_plus,,
+util_total_number_of_tours_is_4+,Total Number of Tours  >= 4,tot_tours >= 4,,,,,,coef_unavailable,coef_unavailable,coef_unavailable
+util_has_mandatory_tours_and_tour_frequency_is_1,One or more Mandatory tour & tour frequency =1,(num_mandatory_tours>0)&(tot_tours == 1),,,,,,,coef_has_mandatory_tours_and_tour_frequency_is_1,
+util_has_mandatory_tours_and_tour_frequency_is_2,One or more Mandatory tour & tour frequency =2,(num_mandatory_tours>0)&(tot_tours == 2),coef_has_mandatory_tours_and_tour_frequency_is_2,coef_has_mandatory_tours_and_tour_frequency_is_2,,,,,,
+util_has_mandatory_tours_and_tour_frequency_is_2+,One or more Mandatory tour & tour frequency =2+,(num_mandatory_tours>0)&(tot_tours >= 2),,,coef_has_mandatory_tours_and_tour_frequency_is_2_plus,,,coef_has_mandatory_tours_and_tour_frequency_is_2_plus,coef_has_mandatory_tours_and_tour_frequency_is_2_plus,coef_has_mandatory_tours_and_tour_frequency_is_2_plus
+util_has_mandatory_tours_and_tour_frequency_is_3,One or more Mandatory tour & tour frequency =3,(num_mandatory_tours>0)&(tot_tours == 3),coef_has_mandatory_tours_and_tour_frequency_is_3,coef_has_mandatory_tours_and_tour_frequency_is_3,,,,,,
+util_has_mandatory_tours_and_tour_frequency_is_4+,One or more Mandatory tour & tour frequency =4+,(num_mandatory_tours>0)&(tot_tours >= 4),coef_has_mandatory_tours_and_tour_frequency_is_4_plus,coef_has_mandatory_tours_and_tour_frequency_is_4_plus,,,,,,
+util_has_joint_tours_and_tour_frequency_is_1,One or more Joint tour & tour frequency =1,(num_person_joint_tours>0)&(tot_tours == 1),,,coef_has_joint_tours_and_tour_frequency_is_1,coef_has_joint_tours_and_tour_frequency_is_1,coef_has_joint_tours_and_tour_frequency_is_1,,,
+util_has_joint_tours_and_tour_frequency_is_2,One or more Joint tour & tour frequency =2,(num_person_joint_tours>0)&(tot_tours == 2),,,,coef_has_joint_tours_and_tour_frequency_is_2,,,,
+util_has_joint_tours_and_tour_frequency_is_3+,One or more Joint tour & tour frequency =3+,(num_person_joint_tours>0)&(tot_tours >= 3),coef_has_joint_tours_and_tour_frequency_is_3_plus,,,coef_has_joint_tours_and_tour_frequency_is_3_plus,,,,
+util_has_joint_tours_and_tour_frequency_is_4+,One or more Joint tour & tour frequency =4+,(num_person_joint_tours>0)&(tot_tours >= 4),,coef_has_joint_tours_and_tour_frequency_is_4_plus,,,,,,
+util_has_joint_tours_and_tour_frequency_is_2+,One or more Joint tour & tour frequency =2+,(num_person_joint_tours>0)&(tot_tours >= 2),,,coef_has_joint_tours_and_tour_frequency_is_2_plus,,coef_has_joint_tours_and_tour_frequency_is_2_plus,,,
+util_has_joint_tours_and_tour_frequency_is_1+,One or more Joint tour & tour frequency =1+,(num_person_joint_tours>0)&(tot_tours >= 1),,,,,,,coef_has_joint_tours_and_tour_frequency_is_1_plus,
+util_number_of_joint_shopping_tours,Number of Joint Shopping tours,(shopping>0) * num_person_joint_shop_tours,coef_number_of_joint_shopping_tours,,,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,,,
+util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,(othmaint>0) * num_person_joint_maint_tours,,,,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,,,
+util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,(eatout>0) * num_person_joint_eatout_tours,,,,,coef_number_of_joint_eating_out_tours,,,
+util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,(othdiscr>0) * num_person_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,,,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,
+util_more_than_one_escorting_tours,More than one escorting tours,escort > 1,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours,,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours
+util_more_than_one_shopping_tours,More than one shopping tours,shopping > 1,,coef_more_than_one_shopping_tours,,coef_more_than_one_shopping_tours,coef_more_than_one_shopping_tours,,,
+util_more_than_one_maintenance_tours,More than one maintenance tours,othmaint>1,,,,coef_more_than_one_maintenance_tours,coef_more_than_one_maintenance_tours,,,
+util_more_than_one_discretionary_tours,More than one discretionary tours,othdiscr > 1,coef_more_than_one_discretionary_tours,coef_more_than_one_discretionary_tours,,coef_more_than_one_discretionary_tours,,coef_more_than_one_discretionary_tours,coef_more_than_one_discretionary_tours,
+util_low_income_group_and_escorting_tour,Dummy for low Income group (> 150K) & escorting tour,low_income * escort,,,coef_low_income_group_and_escorting_tour,,,coef_low_income_group_and_escorting_tour,,
+util_mediumlow_income_group_and_escorting_tour,Dummy for medium low Income group (> 150K) & escorting tour,medium_low_income * escort,,,coef_medium_low_income_group_and_escorting_tour,,,coef_medium_low_income_group_and_escorting_tour,,
+util_mediumhigh_income_group_and_escorting_tour,Dummy for medium high Income group (> 150K) & escorting tour,medium_high_income * escort,,,coef_medium_high_income_group_and_escorting_tour,,,,,
+util_high_income_group_and_escorting_tour,Dummy for high Income group (> 150K) & escorting tour,high_income * escort,coef_high_income_group_and_escorting_tour,coef_high_income_group_and_escorting_tour,coef_high_income_group_and_escorting_tour,,,,,coef_high_income_group_and_escorting_tour
+util_low_income_group_and_shopping_tour,Dummy for low Income group (> 150K) & shopping tour,low_income * shopping,,coef_low_income_group_and_shopping_tour,,coef_low_income_group_and_shopping_tour,,,,
+util_mediumlow_income_group_and_shopping_tour,Dummy for medium low Income group (> 150K) & shopping tour,medium_low_income * shopping,,coef_medium_low_income_group_and_shopping_tour,,coef_medium_low_income_group_and_shopping_tour,,,,
+util_mediumhigh_income_group_and_shopping_tour,Dummy for medium high Income group (> 150K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_medium_high_income_group_and_shopping_tour,coef_medium_high_income_group_and_shopping_tour,,coef_mediumhigh_income_group_and_shopping_tour,,,
+util_high_income_group_and_shopping_tour,Dummy for high Income group (> 150K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,,coef_high_income_group_and_shopping_tour,,coef_high_income_group_and_shopping_tour,
+util_low_income_group_and_maintenance_tour,Dummy for low Income group (<30k) & maintenance tour,low_income * othmaint,coef_low_income_group_and_maintenance_tour,,,,coef_low_income_group_and_maintenance_tour,,,
+util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (30K-60K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,,,,coef_mediumlow_income_group_and_maintenance_tour,,,
+util_mediumhigh_income_group_and_maintenance_tour,Dummy for Medium high Income group (30K-60K) & maintenance tour,medium_high_income * othmaint,,,coef_mediumhigh_income_group_and_maintenance_tour,,,,coef_mediumhigh_income_group_and_maintenance_tour,
+util_high_income_group_and_maintenance_tour,Dummy for High Income group (>150K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,,coef_high_income_group_and_maintenance_tour,,,,coef_high_income_group_and_maintenance_tour,
+util_low_income_group_and_eating_out_tour,Dummy for Low Income group (<30K) & Eating out tour,low_income * eatout,coef_low_income_group_and_eating_out_tour,coef_low_income_group_and_eating_out_tour,,coef_low_income_group_and_eating_out_tour,coef_low_income_group_and_eating_out_tour,,,
+util_medium_low_income_group_and_eating_out_tour,Dummy for medium low Income group (>150K) & Eating out tour,medium_low_income * eatout,,coef_medium_low_income_group_and_eating_out_tour,,,coef_medium_low_income_group_and_eating_out_tour,,,
+util_medium_high_income_group_and_eating_out_tour,Dummy for medium high Income group (>150K) & Eating out tour,medium_high_income * eatout,,,coef_medium_high_income_group_and_eating_out_tour,,,,,
+util_high_income_group_and_eating_out_tour,Dummy for High Income group (>150K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,,,,
+util_low_income_group_and_discretionary_tour,Dummy for Low Income group (<30K) & Discretionary tour,low_income * othdiscr,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,
+util_medium_low_income_group_and_discretionary_tour,Dummy for Medium Low Income group (<30K) & Discretionary tour,medium_low_income * othdiscr,,,,coef_medium_low_income_group_and_discretionary_tour,,,coef_medium_low_income_group_and_discretionary_tour,
+util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (100k-150K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,,
+util_high_income_group_and_discretionary_tour,Dummy for High Income group (>150K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,
+util_low_income_group_and_visiting_tour,Dummy for Low Income group (<30k) & Visiting tour,low_income * social,coef_low_income_group_and_visiting_tour,,coef_low_income_group_and_visiting_tour,,,,coef_low_income_group_and_visiting_tour,
+util_mediumlow_income_group_and_visiting_tour,Dummy for medium Low Income group (<30k) & Visiting tour,medium_low_income * social,,,coef_medium_low_income_group_and_visiting_tour,,,,coef_medium_low_income_group_and_visiting_tour,
+util_high_income_group_and_visiting_tour,Dummy for high Income group (<30k) & Visiting tour,high_income * social,,,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,
+util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,,,,coef_mediumhigh_income_group_and_visiting_tour,,coef_mediumhigh_income_group_and_visiting_tour,,
+util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,,,,
+util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,,
+util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,,,coef_female_and_maintenance_tour,,coef_female_and_maintenance_tour,,,
+util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,,coef_female_and_eatingout_tour,,,coef_female_and_eatingout_tour,,,
+util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,,,,,coef_female_and_discretionary_tour,,,
+util_female_and_discretionary_tour,Dummy for Female & Visiting Tour,female * social,,,coef_female_and_visit_tour,coef_female_and_visit_tour,,,,
+util_zero_car_ownership_and_tour_frequency_is_2+,Dummy for zero car ownership & tour frequency >=2,no_cars & (tot_tours >= 2),coef_zero_car_ownership_and_tour_frequency_is_2_plus,coef_zero_car_ownership_and_tour_frequency_is_2_plus,,coef_zero_car_ownership_and_tour_frequency_is_2_plus,,,,
+util_car_shortage_vs_workers_and_tour_frequency_is_2+,Dummy for Car Shortage vs Workers & tour frequency >=2,~no_cars & (car_sufficiency < 0) & (tot_tours >= 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_2_plus,,,,,,
+util_car_surplus_vs_workers_and_tour_frequency_is_1+,Dummy for Car Surplus vs Workers & tour frequency >=1,~no_cars & (car_sufficiency > 0) & (tot_tours >= 1),,,coef_car_surplus_vs_workers_and_tour_frequency_is_1_plus,,,,,
+util_car_surplus_vs_workers_and_tour_frequency_is_3+,Dummy for Car Surplus vs Workers & tour frequency >=3,~no_cars & (car_sufficiency > 0) & (tot_tours >= 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3_plus,,,,,,,
+util_zero_car_ownership_and_escorting_tour,Dummy for zero car ownership & number of escorting tour,no_cars * escort,coef_zero_car_ownership_and_escorting_tour,,,coef_zero_car_ownership_and_escorting_tour,,,,coef_zero_car_ownership_and_escorting_tour
+util_car_shortage_vs_workers_and_escorting_tour,Dummy for Car Shortage vs Workers & number of escorting tours,(~no_cars & (car_sufficiency < 0)) * escort,coef_car_shortage_vs_workers_and_number_of_escorting_tours,coef_car_shortage_vs_workers_and_number_of_escorting_tours,,,,,,coef_car_shortage_vs_workers_and_number_of_escorting_tours
+util_car_surplus_vs_workers_and_escorting_tour,Dummy for Car Surplus vs Workers & number of escorting tours,(~no_cars & (car_sufficiency > 0)) * escort,,coef_car_surplus_vs_workers_and_number_of_escorting_tours,,coef_car_surplus_vs_workers_and_number_of_escorting_tours,,,,
+util_car_shortage_vs_workers_and_shopping_tour,Dummy for Car Shortage vs Workers & number of shopping tours,(~no_cars & (car_sufficiency < 0)) * shopping,,coef_car_shortage_vs_workers_and_number_of_shopping_tours,,,,,,
+util_car_surplus_vs_workers_and_shopping_tour,Dummy for Car Surplus vs Workers & number of shopping tours,(~no_cars & (car_sufficiency > 0)) * shopping,,coef_car_surplus_vs_workers_and_number_of_shopping_tours,,,,,,
+util_car_surplus_vs_workers_and_maintenance_tour,Dummy for Car Surplus vs Workers & number of maintenance tours,(~no_cars & (car_sufficiency > 0)) * othmaint,,,,,,,coef_car_surplus_vs_workers_and_number_of_maintenance_tours,
+util_car_surplus_vs_workers_and_discretionary_tour,Dummy for Car Surplus vs Workers & number of discretionary tours,(~no_cars & (car_sufficiency > 0)) * othdiscr,,,,,,,coef_car_surplus_vs_workers_and_number_of_discretionary_tours,
+util_number_of_full_time_worker_and_escorting_tour,Dummy for number of Full time Worker (other than modeled person) & Escorting tour ,num_full_time_workers_not_self * escort,coef_number_of_full_time_worker_and_escorting_tour,coef_number_of_full_time_worker_and_escorting_tour,coef_number_of_full_time_worker_and_escorting_tour,coef_number_of_full_time_worker_and_escorting_tour,coef_number_of_full_time_worker_and_escorting_tour,,,
+util_number_of_part_time_worker_and_escorting_tour,Dummy for number of Part time Worker (other than modeled person) & Escorting tour ,num_part_time_workers_not_self * escort,coef_number_of_part_time_worker_and_escorting_tour,coef_number_of_part_time_worker_and_escorting_tour,coef_number_of_part_time_worker_and_escorting_tour,coef_number_of_part_time_worker_and_escorting_tour,coef_number_of_part_time_worker_and_escorting_tour,,,coef_number_of_part_time_worker_and_escorting_tour
+util_number_of_non_worker_and_escorting_tour,Dummy for number of Non-Worker (other than modeled person) & Escorting tour ,num_non_workers_not_self * escort,coef_number_of_non_worker_and_escorting_tour,coef_number_of_non_worker_and_escorting_tour,coef_number_of_non_worker_and_escorting_tour,coef_number_of_non_worker_and_escorting_tour,coef_number_of_non_worker_and_escorting_tour,,,coef_number_of_non_worker_and_escorting_tour
+util_number_of_retiree_and_escorting_tour,Dummy for number of Retiree (other than modeled person) & Escorting tour ,num_retirees_not_self * escort,coef_number_of_retiree_and_escorting_tour,,coef_number_of_retiree_and_escorting_tour,coef_number_of_retiree_and_escorting_tour,,,,
+util_number_of_university_student_and_escorting_tour,Dummy for number of University Student (other than modeled person) & Escorting tour ,num_university_students_not_self * escort,coef_number_of_university_student_and_escorting_tour,coef_number_of_university_student_and_escorting_tour,coef_number_of_university_student_and_escorting_tour,coef_number_of_university_student_and_escorting_tour,,,,coef_number_of_university_student_and_escorting_tour
+util_number_of_driving_school_kid_and_escorting_tour,Dummy for number of Driving School Kid (other than modeled person) & Escorting tour ,num_driving_age_students_not_self * escort,coef_number_of_driving_school_kid_and_escorting_tour,coef_number_of_driving_school_kid_and_escorting_tour,,coef_number_of_driving_school_kid_and_escorting_tour,coef_number_of_driving_school_kid_and_escorting_tour,,,
+util_go_out_pre_driving_school_kid_and_escorting_tour,Dummy for number of Pre-Driving School Kid go out & Escorting tour ,num_pre_driving_age_school_kids_go_out * escort,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour
+util_go_out_pre_school_kid_and_escorting_tour,Dummy for Pre-School Kid going out & Escorting tour ,num_pre_school_kids_go_out * escort,coef_go_out_school_kid_and_escorting_tour,coef_go_out_school_kid_and_escorting_tour,coef_go_out_school_kid_and_escorting_tour,coef_go_out_school_kid_and_escorting_tour,,,,coef_go_out_school_kid_and_escorting_tour
+util_number_of_full_time_worker_and_shopping_tour,Dummy for number of Full time Worker (other than modeled person) & Shopping tour ,num_full_time_workers_not_self * shopping,,coef_number_of_full_time_worker_and_shopping_tour,coef_number_of_full_time_worker_and_shopping_tour,,,coef_number_of_full_time_worker_and_shopping_tour,,
+util_number_of_part_time_worker_and_shopping_tour,Dummy for number of Part time Worker (other than modeled person) & Shopping tour ,num_part_time_workers_not_self * shopping,,coef_number_of_part_time_worker_and_shopping_tour,coef_number_of_part_time_worker_and_shopping_tour,,,coef_number_of_part_time_worker_and_shopping_tour,,
+util_number_of_non_worker_and_shopping_tour,Dummy for number of Non-Worker (other than modeled person) & Shopping tour ,num_non_workers_not_self * shopping,,coef_number_of_non_worker_and_shopping_tour,,coef_number_of_non_worker_and_shopping_tour,,coef_number_of_non_worker_and_shopping_tour,,
+util_number_of_retiree_and_shopping_tour,Dummy for number of Retiree (other than modeled person) & Shopping tour ,num_retirees_not_self * shopping,,,,,coef_number_of_retiree_and_shopping_tour,,,
+util_number_of_university_student_and_shopping_tour,Dummy for number of University Student (other than modeled person) & Shopping tour ,num_university_students_not_self * shopping,,,coef_number_of_university_student_and_shopping_tour,,,,,
+util_number_of_driving_school_kid_and_shopping_tour,Dummy for number of Driving School Kid (other than modeled person) & Shopping tour ,num_driving_age_students_not_self * shopping,coef_number_of_driving_school_kid_and_shopping_tour,,,,,,coef_number_of_driving_school_kid_and_shopping_tour,
+util_number_of_pre_driving_school_kid_and_shopping_tour,Dummy for number of Pre-Driving School Kid (other than modeled person) & Shopping tour ,num_pre_driving_age_school_kids_not_self * shopping,coef_number_of_pre_driving_school_kid_and_shopping_tour,,,,,coef_number_of_pre_driving_school_kid_and_shopping_tour,coef_number_of_pre_driving_school_kid_and_shopping_tour,
+util_number_of_pre_school_kid_and_shopping_tour,Dummy for number of Pre-School Kid (other than modeled person) & Shopping tour ,num_pre_school_kids_not_self * shopping,coef_number_of_pre_school_kid_and_shopping_tour,,,,,coef_number_of_pre_school_kid_and_shopping_tour,coef_number_of_pre_school_kid_and_shopping_tour,
+util_number_of_full_time_worker_and_maintenance_tour,Dummy for number of Full time Worker (other than modeled person) & Maintenance tour ,num_full_time_workers_not_self * othmaint,coef_number_of_full_time_worker_and_maintenance_tour,coef_number_of_full_time_worker_and_maintenance_tour,coef_number_of_full_time_worker_and_maintenance_tour,coef_number_of_full_time_worker_and_maintenance_tour,,,,
+util_number_of_part_time_worker_and_maintenance_tour,Dummy for number of Part time Worker (other than modeled person) & Maintenance tour ,num_part_time_workers_not_self * othmaint,coef_number_of_part_time_worker_and_maintenance_tour,coef_number_of_part_time_worker_and_maintenance_tour,coef_number_of_part_time_worker_and_maintenance_tour,coef_number_of_part_time_worker_and_maintenance_tour,,,,
+util_number_of_non_worker_and_maintenance_tour,Dummy for number of Non-Worker(other than modeled person) & Maintenance tour ,num_non_workers_not_self * othmaint,coef_number_of_non_worker_and_maintenance_tour,coef_number_of_non_worker_and_maintenance_tour,coef_number_of_non_worker_and_maintenance_tour,,,,,
+util_number_of_retiree_and_maintenance_tour,Dummy for number of Retiree (other than modeled person) & Maintenance tour ,num_retirees_not_self * othmaint,,,,,coef_number_of_retiree_and_maintenance_tour,,,
+util_number_of_university_student_and_maintenance_tour,Dummy for number of University Student (other than modeled person) & Maintenance tour ,num_university_students_not_self * othmaint,coef_number_of_university_student_and_maintenance_tour,,coef_number_of_university_student_and_maintenance_tour,,,,,
+util_number_of_full_time_worker_and_eating_out_tour,Dummy for number of Full time Worker (other than modeled person) & Eating Out tour ,num_full_time_workers_not_self * eatout,coef_number_of_full_time_worker_and_eating_out_tour,coef_number_of_full_time_worker_and_eating_out_tour,,,,,,
+util_number_of_part_time_worker_and_eating_out_tour,Dummy for number of Part time Worker (other than modeled person) & Eating Out tour ,num_part_time_workers_not_self * eatout,coef_number_of_part_time_worker_and_eating_out_tour,,,,,,,
+util_number_of_non_worker_and_eating_out_tour,Dummy for number of Non-Worker (other than modeled person) & Eating Out tour ,num_non_workers_not_self * eatout,coef_number_of_non_worker_and_eating_out_tour,,,,,,,
+util_number_of_retiree_and_eating_out_tour,Dummy for number of Retiree (other than modeled person) & Eating Out tour ,num_retirees_not_self * eatout,coef_number_of_retiree_and_eating_out_tour,,,,,,,
+util_number_of_university_student_and_eating_out_tour,Dummy for number of University Student (other than modeled person) & Eating Out tour ,num_university_students_not_self * eatout,coef_number_of_university_student_and_eating_out_tour,,,,,,,
+util_number_of_pre_driving_school_kid_and_eating_out_tour,Dummy for number of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,num_pre_driving_age_school_kids_not_self * eatout,coef_number_of_pre_driving_school_kid_and_eating_out_tour,,,coef_number_of_pre_driving_school_kid_and_eating_out_tour,,,,
+util_number_of_pre_school_kid_and_eating_out_tour,Dummy for number of Pre-School Kid (other than modeled person) & Eating Out tour ,num_pre_school_kids_not_self * eatout,coef_number_of_pre_school_kid_and_eating_out_tour,,,coef_number_of_pre_school_kid_and_eating_out_tour,,,,
+util_number_of_part_time_worker_and_visiting_tour,Dummy for number of Part time Worker (other than modeled person) & Visiting tour ,num_part_time_workers_not_self * social,,coef_number_of_part_time_worker_and_visiting_tour,,,,,,
+util_number_of_non_worker_and_visiting_tour,Dummy for number of Non-Worker (other than modeled person) & Visiting tour ,num_non_workers_not_self * social,coef_number_of_non_worker_and_visiting_tour,,,,,,,
+util_number_of_retiree_and_visiting_tour,Dummy for number of Retiree (other than modeled person) & Visiting tour ,num_retirees_not_self * social,coef_number_of_retiree_and_visiting_tour,coef_number_of_retiree_and_visiting_tour,,,coef_number_of_retiree_and_visiting_tour,,,
+util_number_of_university_student_and_visiting_tour,Dummy for number of University Student (other than modeled person) & Visiting tour ,num_university_students_not_self * social,coef_number_of_university_student_and_visiting_tour,,,,,,,
+util_number_of_driving_school_kid_and_visiting_tour,Dummy for number of Driving School Kid (other than modeled person) & visiting tour ,num_driving_age_students_not_self * social,,coef_number_of_driving_school_kid_and_visiting_tour,,,,,coef_number_of_driving_school_kid_and_visiting_tour,
+util_number_of_pre_driving_school_kid_and_visiting_tour,Dummy for number of Pre-Driving School Kid (other than modeled person) & visiting tour ,num_pre_driving_age_school_kids_not_self * social,,coef_number_of_pre_driving_school_kid_and_visiting_tour,,coef_number_of_pre_driving_school_kid_and_visiting_tour,,,coef_number_of_pre_driving_school_kid_and_visiting_tour,
+util_number_of_pre_school_kid_and_visiting_tour,Dummy for number of Pre-School Kid (other than modeled person) & visiting tour ,num_pre_school_kids_not_self * social,,coef_number_of_pre_school_kid_and_visiting_tour,,coef_number_of_pre_school_kid_and_visiting_tour,,,coef_number_of_pre_school_kid_and_visiting_tour,
+util_number_of_part_time_worker_and_discretionary_tour,Dummy for number of Part time Worker (other than modeled person) & Discretionary tour ,num_part_time_workers_not_self * othdiscr,,,,,,coef_number_of_part_time_worker_and_discretionary_tour,,
+util_number_of_non_worker_and_discretionary_tour,Dummy for number of Non-Worker  (other than modeled person) & Discretionary tour ,num_non_workers_not_self * othdiscr,,,,,,coef_number_of_non_worker_and_discretionary_tour,,
+util_number_of_university_student_and_discretionary_tour,Dummy for number of University Student (other than modeled person) & Discretionary tour ,num_university_students_not_self * othdiscr,,,coef_number_of_university_student_and_discretionary_tour,,,coef_number_of_university_student_and_discretionary_tour,,
+util_number_of_driving_school_kid_and_discretionary_tour,Dummy for number of Driving School Kid (other than modeled person) & Discretionary tour ,num_driving_age_students_not_self * othdiscr,coef_number_of_driving_school_kid_and_discretionary_tour,coef_number_of_driving_school_kid_and_discretionary_tour,,coef_number_of_driving_school_kid_and_discretionary_tour,,,coef_number_of_driving_school_kid_and_discretionary_tour,
+util_number_of_pre_driving_school_kid_and_discretionary_tour,Dummy for number of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,num_pre_driving_age_school_kids_not_self * othdiscr,coef_number_of_pre_driving_school_kid_and_discretionary_tour,coef_number_of_pre_driving_school_kid_and_discretionary_tour,,coef_number_of_pre_driving_school_kid_and_discretionary_tour,,,coef_number_of_pre_driving_school_kid_and_discretionary_tour,
+util_number_of_pre_school_kid_and_discretionary_tour,Dummy for number of Pre-School Kid (other than modeled person) & Discretionary tour ,num_pre_school_kids_not_self * othdiscr,coef_number_of_pre_school_kid_and_discretionary_tour,coef_number_of_pre_school_kid_and_discretionary_tour,,coef_number_of_pre_school_kid_and_discretionary_tour,,,coef_number_of_pre_school_kid_and_discretionary_tour,
+util_retired_hh_and_escorting,"Household Type (All Retirees and Non-workers only), Escorting",retiredHh * escort,,,,,coef_retired_hh_and_escorting_tour,,,
+util_retired_hh_and_shopping,"Household Type (All Retirees and Non-workers only), Shopping",retiredHh * shopping,,,,,coef_retired_hh_and_shopping_tour,,,
+util_retired_hh_and_eating_out,"Household Type (All Retirees and Non-workers only), Eating out",retiredHh * eatout,,,,,coef_retired_hh_and_eating_out_tour,,,
+util_retired_hh_and_discretionary,"Household Type (All Retirees and Non-workers only), Discretionary",retiredHh * othdiscr,,,,,coef_retired_hh_and_discretionary_tour,,,
+util_work_accessibility_and_tour_frequency_is_1,Work Accessibility & Tour Frequency =1,WorkLocationLogsum * ((tot_tours == 1) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_1,coef_work_accessibility_and_tour_frequency_is_1,,,,,,
+util_work_accessibility_and_tour_frequency_is_2,Work Accessibility & Tour Frequency =2,WorkLocationLogsum * ((tot_tours == 2) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_2,coef_work_accessibility_and_tour_frequency_is_2,,,,,,
+util_work_accessibility_and_tour_frequency_is_3,Work Accessibility & Tour Frequency =3,WorkLocationLogsum * ((tot_tours == 3) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_3,,,,,,,
+util_work_accessibility_and_tour_frequency_is_3_plus,Work Accessibility & Tour Frequency =3+,WorkLocationLogsum * ((tot_tours >= 3) & (cdap_activity == 'M')),,coef_work_accessibility_and_tour_frequency_is_3_plus,,,,,,
+util_work_accessibility_and_tour_frequency_is_4,Work Accessibility & Tour Frequency =4,WorkLocationLogsum * ((tot_tours == 4) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_4,,,,,,,
+util_work_accessibility_and_tour_frequency_is_5_plus,Work Accessibility & Tour Frequency =5+,WorkLocationLogsum * ((tot_tours > 5) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_5_plus,,,,,,,
+util_school_accessibility_and_tour_frequency_is_1,School Accessibility & Tour Frequency =1,SchoolLocationLogsum * ((tot_tours == 1) & (cdap_activity == 'M')),,,,,,,coef_school_accessibility_and_tour_frequency_is_1,
+util_school_accessibility_and_tour_frequency_is_1_plus,School Accessibility & Tour Frequency =1+,SchoolLocationLogsum * ((tot_tours >= 1) & (cdap_activity == 'M')),,,coef_school_accessibility_and_tour_frequency_is_1_plus,,,,,
+util_school_accessibility_and_tour_frequency_is_2_plus,School Accessibility & Tour Frequency =2+,SchoolLocationLogsum * ((tot_tours >= 2) & (cdap_activity == 'M')),,,,,,coef_school_accessibility_and_tour_frequency_is_2_plus,coef_school_accessibility_and_tour_frequency_is_2_plus,
+util_work_from_home_and_tour_frequency_is_1,Work From Home & Tour Frequency =1,(WorkLocation==99999) & (tot_tours == 1),coef_work_from_home_and_tour_frequency_is_1,,,,,,,
+util_work_from_home_and_tour_frequency_is_2,Work From Home  & Tour Frequency =2,(WorkLocation==99999) & (tot_tours == 2),coef_work_from_home_and_tour_frequency_is_2,,,,,,,
+util_work_from_home_and_tour_frequency_is_3,Work From Home & Tour Frequency =3,(WorkLocation==99999) & (tot_tours == 3),coef_work_from_home_and_tour_frequency_is_3,,,,,,,
+util_work_from_home_and_tour_frequency_is_4,Work From Home & Tour Frequency =4,(WorkLocation==99999) & (tot_tours == 4),coef_work_from_home_and_tour_frequency_is_4,,,,,,,
+util_work_from_home_and_tour_frequency_is_5_plus,Work From Home & Tour Frequency =5+,(WorkLocation==99999) & (tot_tours > 5),coef_work_from_home_and_tour_frequency_is_5_plus,,,,,,,
+util_retail_accessibility_for_escorting,Retail Accessibility for Escorting,((autos<num_workers)*escort_hov_insufficient_accessibility+(autos==num_workers)*escort_hov_sufficient_accessibility+(autos>num_workers)*escort_hov_oversufficient_accessibility)*escort,coef_retail_accessibility_for_escorting,coef_retail_accessibility_for_escorting,,coef_retail_accessibility_for_escorting,coef_retail_accessibility_for_escorting,,coef_retail_accessibility_for_escorting,coef_retail_accessibility_for_escorting
+util_retail_accessibility_for_shopping,Retail Accessibility for Shopping,((autos<num_workers)*shop_hov_insufficient_accessibility+(autos==num_workers)*shop_hov_sufficient_accessibility+(autos>num_workers)*shop_hov_oversufficient_accessibility)*shopping,coef_retail_accessibility_for_shopping,,,,coef_retail_accessibility_for_shopping,coef_retail_accessibility_for_shopping,coef_retail_accessibility_for_shopping,coef_retail_accessibility_for_shopping
+util_retail_accessibility_for_maintenance,Retail Accessibility for Maintenance,((autos<num_workers)*maint_hov_insufficient_accessibility+(autos==num_workers)*maint_hov_sufficient_accessibility+(autos>num_workers)*maint_hov_oversufficient_accessibility)*othmaint,coef_retail_accessibility_for_maintenance,coef_retail_accessibility_for_maintenance,,coef_retail_accessibility_for_maintenance,coef_retail_accessibility_for_maintenance,,coef_retail_accessibility_for_maintenance,
+util_retail_accessibility_for_eating_out,Retail Accessibility for Eating Out,((autos<num_workers)*eat_hov_insufficient_accessibility+(autos==num_workers)*eat_hov_sufficient_accessibility+(autos>num_workers)*eat_hov_oversufficient_accessibility)*eatout,coef_retail_accessibility_for_eating_out,coef_retail_accessibility_for_eating_out,,,,coef_retail_accessibility_for_eating_out,coef_retail_accessibility_for_eating_out,
+util_retail_accessibility_for_discretionary,Retail Accessibility for Discretionary,((autos<num_workers)*discr_hov_insufficient_accessibility+(autos==num_workers)*discr_hov_sufficient_accessibility+(autos>num_workers)*discr_hov_oversufficient_accessibility)*othdiscr,,,coef_retail_accessibility_for_discretionary,coef_retail_accessibility_for_discretionary,coef_retail_accessibility_for_discretionary,,coef_retail_accessibility_for_discretionary,
+util_retail_accessibility_for_visiting,Retail Accessibility for Visiting,((autos<num_workers)*visit_hov_insufficient_accessibility+(autos==num_workers)*visit_hov_sufficient_accessibility+(autos>num_workers)*visit_hov_oversufficient_accessibility)*othdiscr,,,,coef_retail_accessibility_for_visiting,coef_retail_accessibility_for_visiting,,,
+util_walk_accessibility_for_discretionary,Walk Accessibility for Discretionary,nonmandatory_nm_accessibility *othdiscr,coef_walk_accessibility_for_discretionary,coef_walk_accessibility_for_discretionary,,,,,,
+util_walk_accessibility_for_eat_out,Walk Accessibility for Eating Out,nonmandatory_nm_accessibility *eatout,,,coef_walk_accessibility_for_eatout,coef_walk_accessibility_for_eatout,,,,
+util_origin_population_density_for_visiting,Population Density & Visiting tour,PopDen * social,,coef_origin_population_density_for_visiting,,,,,,
+util_college_education_and_escorting,College Education & Escorting tour,(education_attainment>=13) * escort,,,,,coef_college_education_for_escorting,,,
+util_college_education_and_visiting,College Education & Visiting tour,(education_attainment>=13) * social,coef_college_education_for_visiting,,,,coef_college_education_for_visiting,,,
+util_college_education_and_discretionary,College Education & Discretionary tour,(education_attainment>=13) * othdiscr,coef_college_education_for_discretionary,coef_college_education_for_discretionary,,coef_college_education_for_discretionary,coef_college_education_for_discretionary,,,
+util_college_education_and_shopping,College Education & Shopping tour,(education_attainment>=13) * shopping,,,,coef_college_education_for_shopping,coef_college_education_for_shopping,,,
+util_college_education_and_maintenance,College Education & Maintenance tour,(education_attainment>=13) * othmaint,,,,coef_college_education_for_maintenance,,,,
+util_college_education_and_eating_out,College Education & Eating out tour,(education_attainment>=13) * eatout,,,,coef_college_education_for_eatout,,,,
+util_low_education_and_visiting,Less than High School Education & Visiting tour,(education_attainment<9) * social,coef_low_education_for_visiting,,,coef_low_education_for_visiting,,,,
+util_low_education_and_discretionary,Less than High School Education & Discretionary tour,(education_attainment<9) * othdiscr,,coef_low_education_for_discretionary,,coef_low_education_for_discretionary,coef_low_education_for_discretionary,,,
+util_low_education_and_escorting,Less than High School Education & Escorting tour,(education_attainment<9) * escort,,,,coef_low_education_for_escorting,coef_low_education_for_escorting,,,
+util_low_education_and_shopping,Less than High School Education & Shopping tour,(education_attainment<9) * shopping,,,,coef_low_education_for_shopping,coef_low_education_for_shopping,,,
+util_low_education_and_eating_out,Less than High School Education & Eating out tour,(education_attainment<9) * eatout,,,,coef_low_education_for_eatout,,,,
+util_low_education_and_maintenance,Less than High School Education & Maintenance tour,(education_attainment<9) * othmaint,,,,,coef_low_education_for_maintenance,,,
+util_detached_household_and_escorting_tour,Detached Household & Escorting tour,(building_size==1) * escort,coef_detached_household_and_escorting,coef_detached_household_and_escorting,,,coef_detached_household_and_escorting,,,
+util_detached_household_and_eat_out,Detached Household & Eating out tour,(building_size==1) * eatout,,coef_detached_household_and_eating_out,,,coef_detached_household_and_eating_out,,,
+util_detached_household_and_discretionary,Detached Household & discretionary tour,(building_size==1) * othdiscr,,,coef_detached_household_and_discretionary,,coef_detached_household_and_discretionary,,,
+util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant
+util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant
+util_1_shopping_tours_constant,1 Shopping Tours Constant,shopping == 1,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant
+util_2_plus_shopping_tours_constant,2+ Shopping Tours Constant,shopping >= 2,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant
+util_1_maintenance_tours_constant,1 Maintenance Tours Constant,othmaint == 1,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant
+util_2_plus_maintenance_tours_constant,2+ Maintenance Tours Constant,othmaint >= 2,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant
+util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,(eatout >= 1) * eatout,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant
+util_1_plus_visting_tours_constant,1+ Visting Tours Constant,(social >= 1) * social,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant
+util_1_other_discretionary_tours_constant,1 Other Discretionary Tours Constant,othdiscr == 1,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant
+util_2_plus_other_discretionary_tours_constant,2+ Other Discretionary Tours Constant,othdiscr >= 2,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant
+util_1_escort_tour_constant_tm2,1 Escort Tour constant_tm2,escort == 1,,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2
+util_2_plus_escort_tours_constant_tm2,2+ Escort Tours constant_tm2,escort >= 2,,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2
+util_1_shopping_tours_constant_tm2,1 Shopping Tours constant_tm2,shopping == 1,,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2
+util_2_plus_shopping_tours_constant_tm2,2+ Shopping Tours constant_tm2,shopping >= 2,,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2
+util_1_maintenance_tours_constant_tm2,1 Maintenance Tours constant_tm2,othmaint == 1,,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2
+util_2_plus_maintenance_tours_constant_tm2,2+ Maintenance Tours constant_tm2,othmaint >= 2,,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2
+util_1_plus_eating_out_tours_constant_tm2,1+ Eating Out Tours constant_tm2,(eatout >= 1) * eatout,,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2
+util_1_plus_visting_tours_constant_tm2,1+ Visting Tours constant_tm2,(social >= 1) * social,,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2
+util_1_other_discretionary_tours_constant_tm2,1 Other Discretionary Tours constant_tm2,othdiscr == 1,,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2
+util_2_plus_other_discretionary_tours_constant_tm2,2+ Other Discretionary Tours constant_tm2,othdiscr >= 2,,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2
+util_mandatory_dap_and_1_non_mand_tour_ABM_2_calib,Mandatory and 1 Non-Mandatory - ABM2 Calibration,(cdap_activity=='M') & (tot_tours==1),coef_mandatory_dap_and_1_non_mand_tour,coef_mandatory_dap_and_1_non_mand_tour,coef_mandatory_dap_and_1_non_mand_tour,,,coef_mandatory_dap_and_1_non_mand_tour,coef_mandatory_dap_and_1_non_mand_tour,
+util_mandatory_dap_and_2_non_mand_tour_ABM_2_calib,Mandatory and 2 Non-Mandatory - ABM2 Calibration,(cdap_activity=='M') & (tot_tours==2),coef_mandatory_dap_and_2_non_mand_tour,coef_mandatory_dap_and_2_non_mand_tour,coef_mandatory_dap_and_2_non_mand_tour,,,coef_mandatory_dap_and_2_non_mand_tour,coef_mandatory_dap_and_2_non_mand_tour,
+util_mandatory_dap_and_3_plus_non_mand_tour_ABM_2_calib,Mandatory and 3+ Non-Mandatory - ABM2 Calibration,(cdap_activity=='M') & (tot_tours>=3),coef_mandatory_dap_and_3_plus_non_mand_tour,coef_mandatory_dap_and_3_plus_non_mand_tour,coef_mandatory_dap_and_3_plus_non_mand_tour,,,coef_mandatory_dap_and_3_plus_non_mand_tour,coef_mandatory_dap_and_3_plus_non_mand_tour,
+util_non_mandatory_dap_and_1_non_mand_tour_ABM_2_calib,0 Mandatory and 1 Non-Mandatory - ABM2 Calibration,(cdap_activity=='N') & (tot_tours==1),coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour
+util_non_mandatory_dap_and_2_non_mand_tour_ABM_2_calib,0 Mandatory and 2 Non-Mandatory - ABM2 Calibration,(cdap_activity=='N') & (tot_tours==2),coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour
+util_non_mandatory_dap_and_3_non_mand_tour_ABM_2_calib,0 Mandatory and 3 Non-Mandatory - ABM2 Calibration,(cdap_activity=='N') & (tot_tours==3),coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour
+util_non_mandatory_dap_and_4_plus_non_mand_tour_ABM_2_calib,0 Mandatory and 4+ Non-Mandatory - ABM2 Calibration,(cdap_activity=='N') & (tot_tours>=4),coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.yaml b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.yaml
new file mode 100644
index 000000000..53f56d613
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.yaml
@@ -0,0 +1,52 @@
+
+SEGMENT_COL: ptype
+SPEC: non_mandatory_tour_frequency.csv
+
+SPEC_SEGMENTS:
+  - NAME: PTYPE_FULL
+    PTYPE: 1
+    COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv
+  - NAME: PTYPE_PART
+    PTYPE: 2
+    COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv
+  - NAME: PTYPE_UNIVERSITY
+    PTYPE: 3
+    COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv
+  - NAME: PTYPE_NONWORK
+    PTYPE: 4
+    COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv
+  - NAME: PTYPE_RETIRED
+    PTYPE: 5
+    COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv
+  - NAME: PTYPE_DRIVING
+    PTYPE: 6
+    COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv
+  - NAME: PTYPE_SCHOOL
+    PTYPE: 7
+    COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv
+  - NAME: PTYPE_PRESCHOOL
+    PTYPE: 8
+    COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv
+
+annotate_persons:
+  SPEC: annotate_persons_nmtf
+  DF: persons
+  TABLES:
+    - tours
+
+preprocessor:
+  SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor
+  DF: persons
+  TABLES:
+    - tours
+#    - accessibility
+
+CONSTANTS:
+  PTYPE_FULL: 1
+  PTYPE_PART: 2
+  PTYPE_UNIVERSITY: 3
+  PTYPE_NONWORK: 4
+  PTYPE_RETIRED: 5
+  PTYPE_DRIVING: 6
+  PTYPE_SCHOOL: 7
+  PTYPE_PRESCHOOL: 8
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_alternatives.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_alternatives.csv
new file mode 100644
index 000000000..e4d4f2e4a
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_alternatives.csv
@@ -0,0 +1,198 @@
+escort,shopping,othmaint,eatout,social,othdiscr
+0,0,0,0,0,0
+0,0,0,0,0,1
+0,0,0,0,0,2
+0,0,0,0,1,0
+0,0,0,0,1,1
+0,0,0,0,1,2
+0,0,0,1,0,0
+0,0,0,1,0,1
+0,0,0,1,0,2
+0,0,0,1,1,0
+0,0,0,1,1,1
+0,0,0,1,1,2
+0,0,1,0,0,0
+0,0,1,0,0,1
+0,0,1,0,0,2
+0,0,1,0,1,0
+0,0,1,0,1,1
+0,0,1,0,1,2
+0,0,1,1,0,0
+0,0,1,1,0,1
+0,0,1,1,0,2
+0,0,1,1,1,0
+0,0,1,1,1,1
+0,0,1,1,1,2
+0,0,2,0,0,0
+0,0,2,0,0,1
+0,0,2,0,0,2
+0,0,2,0,1,0
+0,0,2,0,1,1
+0,0,2,0,1,2
+0,0,2,1,0,0
+0,0,2,1,0,1
+0,0,2,1,0,2
+0,0,2,1,1,0
+0,0,2,1,1,1
+0,1,0,0,0,0
+0,1,0,0,0,1
+0,1,0,0,0,2
+0,1,0,0,1,0
+0,1,0,0,1,1
+0,1,0,0,1,2
+0,1,0,1,0,0
+0,1,0,1,0,1
+0,1,0,1,0,2
+0,1,0,1,1,0
+0,1,0,1,1,1
+0,1,0,1,1,2
+0,1,1,0,0,0
+0,1,1,0,0,1
+0,1,1,0,0,2
+0,1,1,0,1,0
+0,1,1,0,1,1
+0,1,1,0,1,2
+0,1,1,1,0,0
+0,1,1,1,0,1
+0,1,1,1,0,2
+0,1,1,1,1,0
+0,1,1,1,1,1
+0,1,2,0,0,0
+0,1,2,0,0,1
+0,1,2,0,0,2
+0,1,2,0,1,0
+0,1,2,0,1,1
+0,1,2,1,0,0
+0,1,2,1,0,1
+0,1,2,1,1,0
+0,2,0,0,0,0
+0,2,0,0,0,1
+0,2,0,0,0,2
+0,2,0,0,1,0
+0,2,0,0,1,1
+0,2,0,0,1,2
+0,2,0,1,0,0
+0,2,0,1,0,1
+0,2,0,1,0,2
+0,2,0,1,1,0
+0,2,0,1,1,1
+0,2,1,0,0,0
+0,2,1,0,0,1
+0,2,1,0,0,2
+0,2,1,0,1,0
+0,2,1,0,1,1
+0,2,1,1,0,0
+0,2,1,1,0,1
+0,2,1,1,1,0
+0,2,2,0,0,0
+0,2,2,0,0,1
+0,2,2,0,1,0
+0,2,2,1,0,0
+1,0,0,0,0,0
+1,0,0,0,0,1
+1,0,0,0,0,2
+1,0,0,0,1,0
+1,0,0,0,1,1
+1,0,0,0,1,2
+1,0,0,1,0,0
+1,0,0,1,0,1
+1,0,0,1,0,2
+1,0,0,1,1,0
+1,0,0,1,1,1
+1,0,0,1,1,2
+1,0,1,0,0,0
+1,0,1,0,0,1
+1,0,1,0,0,2
+1,0,1,0,1,0
+1,0,1,0,1,1
+1,0,1,0,1,2
+1,0,1,1,0,0
+1,0,1,1,0,1
+1,0,1,1,0,2
+1,0,1,1,1,0
+1,0,1,1,1,1
+1,0,2,0,0,0
+1,0,2,0,0,1
+1,0,2,0,0,2
+1,0,2,0,1,0
+1,0,2,0,1,1
+1,0,2,1,0,0
+1,0,2,1,0,1
+1,0,2,1,1,0
+1,1,0,0,0,0
+1,1,0,0,0,1
+1,1,0,0,0,2
+1,1,0,0,1,0
+1,1,0,0,1,1
+1,1,0,0,1,2
+1,1,0,1,0,0
+1,1,0,1,0,1
+1,1,0,1,0,2
+1,1,0,1,1,0
+1,1,0,1,1,1
+1,1,1,0,0,0
+1,1,1,0,0,1
+1,1,1,0,0,2
+1,1,1,0,1,0
+1,1,1,0,1,1
+1,1,1,1,0,0
+1,1,1,1,0,1
+1,1,1,1,1,0
+1,1,2,0,0,0
+1,1,2,0,0,1
+1,1,2,0,1,0
+1,1,2,1,0,0
+1,2,0,0,0,0
+1,2,0,0,0,1
+1,2,0,0,0,2
+1,2,0,0,1,0
+1,2,0,0,1,1
+1,2,0,1,0,0
+1,2,0,1,0,1
+1,2,0,1,1,0
+1,2,1,0,0,0
+1,2,1,0,0,1
+1,2,1,0,1,0
+1,2,1,1,0,0
+1,2,2,0,0,0
+2,0,0,0,0,0
+2,0,0,0,0,1
+2,0,0,0,0,2
+2,0,0,0,1,0
+2,0,0,0,1,1
+2,0,0,0,1,2
+2,0,0,1,0,0
+2,0,0,1,0,1
+2,0,0,1,0,2
+2,0,0,1,1,0
+2,0,0,1,1,1
+2,0,1,0,0,0
+2,0,1,0,0,1
+2,0,1,0,0,2
+2,0,1,0,1,0
+2,0,1,0,1,1
+2,0,1,1,0,0
+2,0,1,1,0,1
+2,0,1,1,1,0
+2,0,2,0,0,0
+2,0,2,0,0,1
+2,0,2,0,1,0
+2,0,2,1,0,0
+2,1,0,0,0,0
+2,1,0,0,0,1
+2,1,0,0,0,2
+2,1,0,0,1,0
+2,1,0,0,1,1
+2,1,0,1,0,0
+2,1,0,1,0,1
+2,1,0,1,1,0
+2,1,1,0,0,0
+2,1,1,0,0,1
+2,1,1,0,1,0
+2,1,1,1,0,0
+2,1,2,0,0,0
+2,2,0,0,0,0
+2,2,0,0,0,1
+2,2,0,0,1,0
+2,2,0,1,0,0
+2,2,1,0,0,0
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv
new file mode 100644
index 000000000..9b63dbc42
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv
@@ -0,0 +1,36 @@
+Description,Target,Expression
+#,,
+,low_income,persons.income<=30000
+,medium_low_income,(persons.income > 30000) & (persons.income <= 60000)
+,medium_income,(persons.income > 60000) & (persons.income <= 100000)
+,medium_high_income,(persons.income > 100000) & (persons.income <= 150000)
+,high_income,(persons.income > 150000)
+,no_cars,(persons.auto_ownership == 0)
+,car_sufficiency,persons.autos-persons.num_workers
+#,,
+# joint tour counts per persons,,
+get joint tours,_JOINT_TOURS,tours[tours.tour_category=='JOINT_NON_MANDATORY']
+create participant list in joint tours,_JOINT_TOURS,"_JOINT_TOURS.assign(person_num=_JOINT_TOURS.apply(lambda x: [int(p) for p in x.tour_participants.split(' ')],axis=1))"
+explode joint tour participants,_JOINT_TOURS,_JOINT_TOURS.explode('person_num')
+join joint tour info with persons,_JOINT_TOURS_PERSONS,"pd.merge(_JOINT_TOURS[['household_id','person_num','tour_purpose']],persons.reset_index()[['household_id','person_num','person_id']], on=['household_id','person_num'], how='left')"
+,num_person_joint_tours,"reindex_i(_JOINT_TOURS_PERSONS.groupby(['person_id']).size(), persons.index)"
+,num_person_joint_shop_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Shop'].groupby(['person_id']).size(), persons.index)"
+,num_person_joint_maint_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Maintenance'].groupby(['person_id']).size(), persons.index)"
+,num_person_joint_eatout_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Eating Out'].groupby(['person_id']).size(), persons.index)"
+,num_person_joint_visit_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Visiting'].groupby(['person_id']).size(), persons.index)"
+,num_person_joint_othdiscr_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Discretionary'].groupby(['person_id']).size(), persons.index)"
+# non_mandatory tour frequency extension,,
+,_INDIV_TOURS,tours[tours.tour_category!='JOINT_NON_MANDATORY']
+,num_mandatory_tours,"reindex_i(_INDIV_TOURS[_INDIV_TOURS.tour_purpose=='MANDATORY'].groupby('person_id').size(), persons.index)"
+,has_mandatory_tour,(num_mandatory_tours > 0) * 1
+,has_joint_tour,(num_person_joint_tours > 0) * 1
+# number of person types in household in addition to self,,
+,num_full_time_workers_not_self,"np.where(df.ptype == PTYPE_FULL, df.num_full_time_workers-1, df.num_full_time_workers)"
+,num_part_time_workers_not_self,"np.where(df.ptype == PTYPE_PART, df.num_part_time_workers-1, df.num_part_time_workers)"
+,num_university_students_not_self,"np.where(df.ptype == PTYPE_UNIVERSITY, df.num_university_students-1, df.num_university_students)"
+,num_non_workers_not_self,"np.where(df.ptype == PTYPE_NONWORK, df.num_non_workers-1, df.num_non_workers)"
+,num_retirees_not_self,"np.where(df.ptype == PTYPE_RETIRED, df.num_retirees-1, df.num_retirees)"
+,num_driving_age_students_not_self,"np.where(df.ptype == PTYPE_DRIVING, df.num_driving_age_students-1, df.num_driving_age_students)"
+,num_pre_driving_age_school_kids_not_self,"np.where(df.ptype == PTYPE_SCHOOL, df.num_pre_driving_age_school_kids-1, df.num_pre_driving_age_school_kids)"
+,num_pre_school_kids_not_self,"np.where(df.ptype == PTYPE_PRESCHOOL, df.num_pre_school_kids-1, df.num_pre_school_kids)"
+,retiredHh,"np.where(df.num_full_time_workers+df.num_part_time_workers+df.num_university_students+df.num_driving_age_students+df.num_pre_driving_age_school_kids+df.num_pre_school_kids == 0,1,0)"
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv
new file mode 100644
index 000000000..7d443b83a
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv
@@ -0,0 +1,60 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_escorting_tour,-5.0011,F
+coef_discretionary_tour,-2.0687,F
+coef_shopping_tour,-16.7785,F
+coef_maintenance_tour,-3.2583,F
+coef_visiting_or_social_tour,-2.6366,F
+coef_eating_out_tour,-18.1598,F
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1_plus,-0.4825,F
+coef_has_mandatory_tours_and_tour_frequency_is_2_plus,-2.5161,F
+coef_number_of_joint_discretionary_tours,-0.2308,F
+coef_more_than_one_discretionary_tours,-1.1938,F
+coef_low_income_group_and_escorting_tour,1.1208,F
+coef_medium_low_income_group_and_escorting_tour,1.1208,F
+coef_low_income_group_and_discretionary_tour,-0.8053,F
+coef_mediumhigh_income_group_and_discretionary_tour,0.5225,F
+coef_high_income_group_and_discretionary_tour,0.8604,F
+coef_high_income_group_and_visiting_tour,0.5513,F
+coef_mediumhigh_income_group_and_visiting_tour,0.5513,F
+coef_female_and_shopping_tour,0.9646,F
+coef_go_out_pre_driving_school_kid_and_escorting_tour,0.4901,F
+coef_number_of_full_time_worker_and_shopping_tour,0.3173,F
+coef_number_of_part_time_worker_and_shopping_tour,0.3173,F
+coef_number_of_non_worker_and_shopping_tour,0.3173,F
+coef_number_of_pre_driving_school_kid_and_shopping_tour,0.3133,F
+coef_number_of_pre_school_kid_and_shopping_tour,0.3133,F
+coef_number_of_part_time_worker_and_discretionary_tour,0.3202,F
+coef_number_of_non_worker_and_discretionary_tour,0.3202,F
+coef_number_of_university_student_and_discretionary_tour,0.5522,F
+coef_school_accessibility_and_tour_frequency_is_2_plus,1.9398,F
+coef_retail_accessibility_for_shopping,0.7809,F
+coef_retail_accessibility_for_eating_out,1.0093,F
+coef_1_escort_tour_constant,-0.052,F
+coef_2_plus_escort_tours_constant,0,T
+coef_1_shopping_tours_constant,1.025,F
+coef_2_plus_shopping_tours_constant,0,T
+coef_1_maintenance_tours_constant,-0.294,F
+coef_2_plus_maintenance_tours_constant,0,T
+coef_1_plus_eating_out_tours_constant,-1.079,F
+coef_1_plus_visting_tours_constant,-0.107,F
+coef_1_other_discretionary_tours_constant,-0.393,F
+coef_2_plus_other_discretionary_tours_constant,-1.086,F
+coef_1_escort_tour_constant_tm2,-1.007,F
+coef_2_plus_escort_tours_constant_tm2,0,T
+coef_1_shopping_tours_constant_tm2,1.027,F
+coef_2_plus_shopping_tours_constant_tm2,1.027,F
+coef_1_maintenance_tours_constant_tm2,-0.196,F
+coef_2_plus_maintenance_tours_constant_tm2,-0.196,F
+coef_1_plus_eating_out_tours_constant_tm2,2.909,F
+coef_1_plus_visting_tours_constant_tm2,-1.417,F
+coef_1_other_discretionary_tours_constant_tm2,-1.519,F
+coef_2_plus_other_discretionary_tours_constant_tm2,-1.519,F
+coef_mandatory_dap_and_1_non_mand_tour,1.222628436,F
+coef_mandatory_dap_and_2_non_mand_tour,0.593278832,F
+coef_mandatory_dap_and_3_plus_non_mand_tour,0.070965231,F
+coef_non_mandatory_dap_and_1_non_mand_tour,0.756307862,F
+coef_non_mandatory_dap_and_2_non_mand_tour,1.396284118,F
+coef_non_mandatory_dap_and_3_non_mand_tour,3.551847757,F
+coef_non_mandatory_dap_and_4_plus_non_mand_tour,-0.058600545,F
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv
new file mode 100644
index 000000000..bfe82bc6b
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv
@@ -0,0 +1,103 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_escorting_tour,-8.005637824,F
+coef_discretionary_tour,-4.191895252,F
+coef_shopping_tour,-6.093236864,F
+coef_maintenance_tour,-4.450826306,F
+coef_visiting_or_social_tour,-3.869879887,F
+coef_eating_out_tour,-12.62493048,F
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,0,T
+coef_total_number_of_tours_is_2,1.328670418,F
+coef_total_number_of_tours_is_3,3.027432477,F
+coef_total_number_of_tours_is_4,3.547389909,F
+coef_has_mandatory_tours_and_tour_frequency_is_2,-1.122394705,F
+coef_has_mandatory_tours_and_tour_frequency_is_3,-2.057588493,F
+coef_has_mandatory_tours_and_tour_frequency_is_4_plus,-2.057588493,F
+coef_has_joint_tours_and_tour_frequency_is_3_plus,-0.477609877,F
+coef_number_of_joint_shopping_tours,-0.170864341,F
+coef_number_of_joint_discretionary_tours,-1.163664908,F
+coef_more_than_one_escorting_tours,0.613851731,F
+coef_more_than_one_discretionary_tours,-0.236694132,F
+coef_high_income_group_and_escorting_tour,-0.066520907,F
+coef_mediumhigh_income_group_and_shopping_tour,0.06372943,F
+coef_high_income_group_and_shopping_tour,0.06372943,F
+coef_low_income_group_and_maintenance_tour,0.251347332,F
+coef_mediumlow_income_group_and_maintenance_tour,0.188910438,F
+coef_high_income_group_and_maintenance_tour,-0.231879121,F
+coef_low_income_group_and_eating_out_tour,-1.810193362,F
+coef_high_income_group_and_eating_out_tour,0.31465606,F
+coef_low_income_group_and_discretionary_tour,-0.565623738,F
+coef_mediumhigh_income_group_and_discretionary_tour,-0.231043198,F
+coef_high_income_group_and_discretionary_tour,0.107170058,F
+coef_low_income_group_and_visiting_tour,0.178971977,F
+coef_female_and_escorting_tour,0.137391737,F
+coef_zero_car_ownership_and_tour_frequency_is_2_plus,-0.935804805,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_2_plus,-0.361005701,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_3_plus,0.072613168,F
+coef_zero_car_ownership_and_escorting_tour,-0.873271814,F
+coef_car_shortage_vs_workers_and_number_of_escorting_tours,0.412574716,F
+coef_number_of_full_time_worker_and_escorting_tour,0.212097217,F
+coef_number_of_part_time_worker_and_escorting_tour,-0.073067961,F
+coef_number_of_non_worker_and_escorting_tour,-0.323356663,F
+coef_number_of_retiree_and_escorting_tour,-0.558947959,F
+coef_number_of_university_student_and_escorting_tour,0.275136763,F
+coef_number_of_driving_school_kid_and_escorting_tour,0.579434039,F
+coef_go_out_pre_driving_school_kid_and_escorting_tour,0.639740336,F
+coef_go_out_school_kid_and_escorting_tour,0.384950782,F
+coef_number_of_driving_school_kid_and_shopping_tour,-0.218745414,F
+coef_number_of_pre_driving_school_kid_and_shopping_tour,-0.218745414,F
+coef_number_of_pre_school_kid_and_shopping_tour,-0.218745414,F
+coef_number_of_full_time_worker_and_maintenance_tour,-0.219097089,F
+coef_number_of_part_time_worker_and_maintenance_tour,-0.219097089,F
+coef_number_of_non_worker_and_maintenance_tour,-0.219097089,F
+coef_number_of_university_student_and_maintenance_tour,-0.219097089,F
+coef_number_of_full_time_worker_and_eating_out_tour,-0.764033574,F
+coef_number_of_part_time_worker_and_eating_out_tour,-0.764033574,F
+coef_number_of_non_worker_and_eating_out_tour,-0.764033574,F
+coef_number_of_retiree_and_eating_out_tour,-0.764033574,F
+coef_number_of_university_student_and_eating_out_tour,-0.764033574,F
+coef_number_of_pre_driving_school_kid_and_eating_out_tour,-0.355618746,F
+coef_number_of_pre_school_kid_and_eating_out_tour,-0.355618746,F
+coef_number_of_non_worker_and_visiting_tour,-0.444931696,F
+coef_number_of_retiree_and_visiting_tour,-0.444931696,F
+coef_number_of_university_student_and_visiting_tour,-0.444931696,F
+coef_number_of_driving_school_kid_and_discretionary_tour,-0.021518881,F
+coef_number_of_pre_driving_school_kid_and_discretionary_tour,-0.021518881,F
+coef_number_of_pre_school_kid_and_discretionary_tour,-0.157036558,F
+coef_work_accessibility_and_tour_frequency_is_1,0.3286661,F
+coef_work_accessibility_and_tour_frequency_is_2,0.71855271,F
+coef_work_accessibility_and_tour_frequency_is_3,0.71855271,F
+coef_work_accessibility_and_tour_frequency_is_4,0.71855271,F
+coef_work_accessibility_and_tour_frequency_is_5_plus,0.71855271,F
+coef_work_from_home_and_tour_frequency_is_1,0.915860359,F
+coef_work_from_home_and_tour_frequency_is_2,1.100337176,F
+coef_work_from_home_and_tour_frequency_is_3,1.100337176,F
+coef_work_from_home_and_tour_frequency_is_4,1.100337176,F
+coef_work_from_home_and_tour_frequency_is_5_plus,1.100337176,F
+coef_retail_accessibility_for_escorting,0.291279719,F
+coef_retail_accessibility_for_shopping,0.205237982,F
+coef_retail_accessibility_for_maintenance,0.097381625,F
+coef_retail_accessibility_for_eating_out,0.678030129,F
+coef_walk_accessibility_for_discretionary,0.045591079,F
+coef_college_education_for_visiting,-0.671955208,F
+coef_college_education_for_discretionary,0.621905223,F
+coef_low_education_for_visiting,0.454831607,F
+coef_detached_household_and_escorting,0.22019233,F
+coef_1_escort_tour_constant,0.390268916,F
+coef_2_plus_escort_tours_constant,0.779302172,F
+coef_1_shopping_tours_constant,0.264,F
+coef_2_plus_shopping_tours_constant,0.383672128,F
+coef_1_maintenance_tours_constant,-0.097866802,F
+coef_2_plus_maintenance_tours_constant,-0.090890202,F
+coef_1_plus_eating_out_tours_constant,-0.974733991,F
+coef_1_plus_visting_tours_constant,-0.147867593,F
+coef_1_other_discretionary_tours_constant,0.037252469,F
+coef_2_plus_other_discretionary_tours_constant,0.221679699,F
+coef_mandatory_dap_and_1_non_mand_tour,0.613102892,F
+coef_mandatory_dap_and_2_non_mand_tour,1.298340523,F
+coef_mandatory_dap_and_3_plus_non_mand_tour,0.294678801,F
+coef_non_mandatory_dap_and_1_non_mand_tour,0.40551385,F
+coef_non_mandatory_dap_and_2_non_mand_tour,-0.058684635,F
+coef_non_mandatory_dap_and_3_non_mand_tour,0.181724483,F
+coef_non_mandatory_dap_and_4_plus_non_mand_tour,3.456313043,F
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv
new file mode 100644
index 000000000..4d022be71
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv
@@ -0,0 +1,90 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_escorting_tour,-5.5709,F
+coef_discretionary_tour,-3.1828,F
+coef_shopping_tour,-1.5833,F
+coef_maintenance_tour,-2.0214,F
+coef_visiting_or_social_tour,-6.6293,F
+coef_eating_out_tour,-8.9856,F
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_3,-0.793922128,F
+coef_total_number_of_tours_is_4,-1.117938236,F
+coef_has_joint_tours_and_tour_frequency_is_1,-0.968579459,F
+coef_has_joint_tours_and_tour_frequency_is_2,-1.808495529,F
+coef_has_joint_tours_and_tour_frequency_is_3_plus,-2.59200638,F
+coef_number_of_joint_shopping_tours,-0.466843362,F
+coef_number_of_joint_maintenance_tours,-0.575337406,F
+coef_more_than_one_escorting_tours,1.3605,F
+coef_more_than_one_shopping_tours,-0.7454,F
+coef_more_than_one_maintenance_tours,-0.1194,F
+coef_more_than_one_discretionary_tours,0.1253,F
+coef_low_income_group_and_shopping_tour,-0.2599,F
+coef_medium_low_income_group_and_shopping_tour,-0.2599,F
+coef_low_income_group_and_eating_out_tour,-0.2857,F
+coef_high_income_group_and_eating_out_tour,2.223,F
+coef_low_income_group_and_discretionary_tour,-0.5775,F
+coef_medium_low_income_group_and_discretionary_tour,-0.5775,F
+coef_high_income_group_and_visiting_tour,0.2695,F
+coef_mediumhigh_income_group_and_visiting_tour,0.2695,F
+coef_female_and_escorting_tour,0.6769,F
+coef_female_and_shopping_tour,0.5108,F
+coef_female_and_visit_tour,0.7494,F
+coef_zero_car_ownership_and_tour_frequency_is_2_plus,-1.0038,F
+coef_zero_car_ownership_and_escorting_tour,-0.8907,F
+coef_car_surplus_vs_workers_and_number_of_escorting_tours,0.1802,F
+coef_number_of_full_time_worker_and_escorting_tour,0.2514,F
+coef_number_of_part_time_worker_and_escorting_tour,0.2512,F
+coef_number_of_non_worker_and_escorting_tour,-0.3489,F
+coef_number_of_retiree_and_escorting_tour,0.2841,F
+coef_number_of_university_student_and_escorting_tour,0.4583,F
+coef_number_of_driving_school_kid_and_escorting_tour,0.7483,F
+coef_go_out_pre_driving_school_kid_and_escorting_tour,0.9378,F
+coef_go_out_school_kid_and_escorting_tour,0.9374,F
+coef_number_of_non_worker_and_shopping_tour,-0.478,F
+coef_number_of_full_time_worker_and_maintenance_tour,0.0904,F
+coef_number_of_part_time_worker_and_maintenance_tour,0.0904,F
+coef_number_of_pre_driving_school_kid_and_eating_out_tour,-0.6629,F
+coef_number_of_pre_school_kid_and_eating_out_tour,-0.6629,F
+coef_number_of_pre_driving_school_kid_and_visiting_tour,-0.4021,F
+coef_number_of_pre_school_kid_and_visiting_tour,-0.4021,F
+coef_number_of_driving_school_kid_and_discretionary_tour,-0.2039,F
+coef_number_of_pre_driving_school_kid_and_discretionary_tour,-0.2039,F
+coef_number_of_pre_school_kid_and_discretionary_tour,-0.2039,F
+coef_retail_accessibility_for_escorting,0.1609,F
+coef_retail_accessibility_for_maintenance,0.0194,F
+coef_retail_accessibility_for_discretionary,0.0632,F
+coef_retail_accessibility_for_visiting,0.2883,F
+coef_walk_accessibility_for_eatout,0.6073,F
+coef_college_education_for_discretionary,0.981,F
+coef_college_education_for_shopping,0.3677,F
+coef_college_education_for_maintenance,0.5982,F
+coef_college_education_for_eatout,0.7979,F
+coef_low_education_for_visiting,-1.6201,F
+coef_low_education_for_discretionary,-0.837,F
+coef_low_education_for_escorting,0.1748,F
+coef_low_education_for_shopping,-0.3525,F
+coef_low_education_for_eatout,-0.4838,F
+coef_1_escort_tour_constant,-2.501,F
+coef_2_plus_escort_tours_constant,-1.958,F
+coef_1_shopping_tours_constant,-2.666,F
+coef_2_plus_shopping_tours_constant,-2.884,F
+coef_1_maintenance_tours_constant,-2.908,F
+coef_2_plus_maintenance_tours_constant,-2.97,F
+coef_1_plus_eating_out_tours_constant,-3.022,F
+coef_1_plus_visting_tours_constant,-2.736,F
+coef_1_other_discretionary_tours_constant,-2.612,F
+coef_2_plus_other_discretionary_tours_constant,-2.663,F
+coef_1_escort_tour_constant_tm2,-1.52187411,F
+coef_2_plus_escort_tours_constant_tm2,-1.489501032,F
+coef_1_shopping_tours_constant_tm2,-1.853328287,F
+coef_2_plus_shopping_tours_constant_tm2,-1.853328287,F
+coef_1_maintenance_tours_constant_tm2,-0.808408856,F
+coef_2_plus_maintenance_tours_constant_tm2,-0.808408856,F
+coef_1_plus_eating_out_tours_constant_tm2,0.600524339,F
+coef_1_plus_visting_tours_constant_tm2,-0.284937967,F
+coef_1_other_discretionary_tours_constant_tm2,-0.504897539,F
+coef_2_plus_other_discretionary_tours_constant_tm2,-0.504897539,F
+coef_non_mandatory_dap_and_1_non_mand_tour,0.563996011,F
+coef_non_mandatory_dap_and_2_non_mand_tour,1.1354493,F
+coef_non_mandatory_dap_and_3_non_mand_tour,1.647365455,F
+coef_non_mandatory_dap_and_4_plus_non_mand_tour,6.448243189,F
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv
new file mode 100644
index 000000000..2d32768ef
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv
@@ -0,0 +1,103 @@
+coefficient_name,value,Constrain
+coef_unavailable,-999,T
+coef_escorting_tour,-6.825870475,F
+coef_discretionary_tour,-3.103671535,F
+coef_shopping_tour,-2.585699928,F
+coef_maintenance_tour,-4.876815454,F
+coef_visiting_or_social_tour,-4.787916747,F
+coef_eating_out_tour,-8.091973006,F
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,0,T
+coef_total_number_of_tours_is_2,1.347662088,F
+coef_total_number_of_tours_is_3,1.958684542,F
+coef_total_number_of_tours_is_4,3.406917203,F
+coef_total_number_of_tours_is_5,3.406917203,F
+coef_total_number_of_tours_is_6_plus,3.406917203,F
+coef_has_mandatory_tours_and_tour_frequency_is_2,-1.383795025,F
+coef_has_mandatory_tours_and_tour_frequency_is_3,-1.383795025,F
+coef_has_mandatory_tours_and_tour_frequency_is_4_plus,-2.308089044,F
+coef_has_joint_tours_and_tour_frequency_is_4_plus,-0.850963319,F
+coef_number_of_joint_discretionary_tours,-0.13985943,F
+coef_more_than_one_escorting_tours,0.688722128,F
+coef_more_than_one_shopping_tours,-0.806980084,F
+coef_more_than_one_discretionary_tours,-1.123565611,F
+coef_high_income_group_and_escorting_tour,-0.188741999,F
+coef_low_income_group_and_shopping_tour,-0.584603787,F
+coef_medium_low_income_group_and_shopping_tour,-0.191476033,F
+coef_medium_high_income_group_and_shopping_tour,0.302369299,F
+coef_high_income_group_and_shopping_tour,0.302369299,F
+coef_low_income_group_and_eating_out_tour,-1.70963028,F
+coef_medium_low_income_group_and_eating_out_tour,-1.018936807,F
+coef_low_income_group_and_discretionary_tour,-0.697953689,F
+coef_mediumhigh_income_group_and_discretionary_tour,0.441095148,F
+coef_high_income_group_and_discretionary_tour,0.441095148,F
+coef_female_and_escorting_tour,0.827860636,F
+coef_female_and_shopping_tour,0.650180816,F
+coef_female_and_eatingout_tour,-1.25440798,F
+coef_zero_car_ownership_and_tour_frequency_is_2_plus,-2.213461849,F
+coef_car_shortage_vs_workers_and_tour_frequency_is_2_plus,-0.490152764,F
+coef_car_shortage_vs_workers_and_number_of_escorting_tours,0.245040809,F
+coef_car_surplus_vs_workers_and_number_of_escorting_tours,-0.273518269,F
+coef_car_shortage_vs_workers_and_number_of_shopping_tours,0.377878264,F
+coef_car_surplus_vs_workers_and_number_of_shopping_tours,-0.579935901,F
+coef_number_of_full_time_worker_and_escorting_tour,0.097720305,F
+coef_number_of_part_time_worker_and_escorting_tour,-0.13505138,F
+coef_number_of_non_worker_and_escorting_tour,-0.264726717,F
+coef_number_of_university_student_and_escorting_tour,0.324362136,F
+coef_number_of_driving_school_kid_and_escorting_tour,0.592250815,F
+coef_go_out_pre_driving_school_kid_and_escorting_tour,0.807393603,F
+coef_go_out_school_kid_and_escorting_tour,0.584027557,F
+coef_number_of_full_time_worker_and_shopping_tour,-0.571692163,F
+coef_number_of_part_time_worker_and_shopping_tour,-0.571692163,F
+coef_number_of_non_worker_and_shopping_tour,-0.571692163,F
+coef_number_of_full_time_worker_and_maintenance_tour,-0.242545271,F
+coef_number_of_part_time_worker_and_maintenance_tour,-0.242545271,F
+coef_number_of_non_worker_and_maintenance_tour,-1.173436234,F
+coef_number_of_full_time_worker_and_eating_out_tour,-0.273301702,F
+coef_number_of_part_time_worker_and_visiting_tour,0.887755866,F
+coef_number_of_retiree_and_visiting_tour,0.885418039,F
+coef_number_of_driving_school_kid_and_visiting_tour,0.023898086,F
+coef_number_of_pre_driving_school_kid_and_visiting_tour,0.023898086,F
+coef_number_of_pre_school_kid_and_visiting_tour,0.023898086,F
+coef_number_of_driving_school_kid_and_discretionary_tour,0.257383748,F
+coef_number_of_pre_driving_school_kid_and_discretionary_tour,0.257383748,F
+coef_number_of_pre_school_kid_and_discretionary_tour,-2.431661289,F
+coef_work_accessibility_and_tour_frequency_is_1,0.628740481,F
+coef_work_accessibility_and_tour_frequency_is_2,0.842604039,F
+coef_work_accessibility_and_tour_frequency_is_3_plus,0.814255739,F
+coef_retail_accessibility_for_escorting,0.199705264,F
+coef_retail_accessibility_for_maintenance,0.189588653,F
+coef_retail_accessibility_for_eating_out,0.387577711,F
+coef_walk_accessibility_for_discretionary,0.032230313,F
+coef_origin_population_density_for_visiting,0.048808797,F
+coef_college_education_for_discretionary,0.256548173,F
+coef_low_education_for_discretionary,-0.974189529,F
+coef_detached_household_and_escorting,0.290441415,F
+coef_detached_household_and_eating_out,-0.227906236,F
+coef_1_escort_tour_constant,0.444537569,F
+coef_2_plus_escort_tours_constant,1.45203917,F
+coef_1_shopping_tours_constant,0.19959348,F
+coef_2_plus_shopping_tours_constant,0.365593218,F
+coef_1_maintenance_tours_constant,-0.050890967,F
+coef_2_plus_maintenance_tours_constant,0.025818889,F
+coef_1_plus_eating_out_tours_constant,-0.688218447,F
+coef_1_plus_visting_tours_constant,-0.761523103,F
+coef_1_other_discretionary_tours_constant,0.151171319,F
+coef_2_plus_other_discretionary_tours_constant,0.349908582,F
+coef_1_escort_tour_constant_tm2,-1.041387049,F
+coef_2_plus_escort_tours_constant_tm2,-1.209189663,F
+coef_1_shopping_tours_constant_tm2,-1.496588445,F
+coef_2_plus_shopping_tours_constant_tm2,-1.496588445,F
+coef_1_maintenance_tours_constant_tm2,-0.067949689,F
+coef_2_plus_maintenance_tours_constant_tm2,-0.067949689,F
+coef_1_plus_eating_out_tours_constant_tm2,0.534488324,F
+coef_1_plus_visting_tours_constant_tm2,-0.010969663,F
+coef_1_other_discretionary_tours_constant_tm2,-0.530568731,F
+coef_2_plus_other_discretionary_tours_constant_tm2,-0.530568731,F
+coef_mandatory_dap_and_1_non_mand_tour,0.435298756,F
+coef_mandatory_dap_and_2_non_mand_tour,1.193250166,F
+coef_mandatory_dap_and_3_plus_non_mand_tour,1.803605616,F
+coef_non_mandatory_dap_and_1_non_mand_tour,0.612888101,F
+coef_non_mandatory_dap_and_2_non_mand_tour,0.745415157,F
+coef_non_mandatory_dap_and_3_non_mand_tour,1.266755805,F
+coef_non_mandatory_dap_and_4_plus_non_mand_tour,2.308013647,F
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv
new file mode 100644
index 000000000..e3f9dce15
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv
@@ -0,0 +1,47 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_escorting_tour,-7.2688,F
+coef_discretionary_tour,-4.2006,F
+coef_shopping_tour,-43.4464,F
+coef_maintenance_tour,-3.3790,F
+coef_visiting_or_social_tour,-4.0352,F
+coef_eating_out_tour,-5.2924,F
+coef_total_number_of_tours_is_0_prior_tours,0.0000,T
+coef_total_number_of_tours_is_2,-0.7216,F
+coef_total_number_of_tours_is_3,-0.9586,F
+coef_has_mandatory_tours_and_tour_frequency_is_2_plus,-2.1108,F
+coef_more_than_one_escorting_tours,2.3269,F
+coef_high_income_group_and_escorting_tour,-0.6921,F
+coef_zero_car_ownership_and_escorting_tour,-0.9936,F
+coef_car_shortage_vs_workers_and_number_of_escorting_tours,-0.9936,F
+coef_number_of_part_time_worker_and_escorting_tour,0.7831,F
+coef_number_of_non_worker_and_escorting_tour,0.7831,F
+coef_number_of_university_student_and_escorting_tour,0.8044,F
+coef_go_out_pre_driving_school_kid_and_escorting_tour,0.5152,F
+coef_go_out_school_kid_and_escorting_tour,0.6072,F
+coef_retail_accessibility_for_escorting,0.2739,F
+coef_retail_accessibility_for_shopping,2.7431,F
+coef_1_escort_tour_constant,-8.402,F
+coef_2_plus_escort_tours_constant,-9.519,F
+coef_1_shopping_tours_constant,-3.856,F
+coef_2_plus_shopping_tours_constant,0.000,T
+coef_1_maintenance_tours_constant,-7.785,F
+coef_2_plus_maintenance_tours_constant,-6.925,F
+coef_1_plus_eating_out_tours_constant,-7.585,F
+coef_1_plus_visting_tours_constant,-7.599,F
+coef_1_other_discretionary_tours_constant,-7.462,F
+coef_2_plus_other_discretionary_tours_constant,-15.000,F
+coef_1_escort_tour_constant_tm2,-1.496,F
+coef_2_plus_escort_tours_constant_tm2,-1.588,F
+coef_1_shopping_tours_constant_tm2,0.031,F
+coef_2_plus_shopping_tours_constant_tm2,0.031,F
+coef_1_maintenance_tours_constant_tm2,-1.011,F
+coef_2_plus_maintenance_tours_constant_tm2,-1.011,F
+coef_1_plus_eating_out_tours_constant_tm2,-1.923,F
+coef_1_plus_visting_tours_constant_tm2,-1.085,F
+coef_1_other_discretionary_tours_constant_tm2,-1.126,F
+coef_2_plus_other_discretionary_tours_constant_tm2,-1.126,F
+coef_non_mandatory_dap_and_1_non_mand_tour,1.113941467,F
+coef_non_mandatory_dap_and_2_non_mand_tour,0.375527882,F
+coef_non_mandatory_dap_and_3_non_mand_tour,2.691413911,F
+coef_non_mandatory_dap_and_4_plus_non_mand_tour,8.404994214,F
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv
new file mode 100644
index 000000000..896947920
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv
@@ -0,0 +1,84 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_escorting_tour,-10.4405,F
+coef_discretionary_tour,-6.1189,F
+coef_shopping_tour,-4.6661,F
+coef_maintenance_tour,-2.4724,F
+coef_visiting_or_social_tour,-3.677,F
+coef_eating_out_tour,-2.5648,F
+coef_total_number_of_tours_is_0_prior_tours,-1.140675606,F
+coef_has_joint_tours_and_tour_frequency_is_1,-1.601015729,F
+coef_has_joint_tours_and_tour_frequency_is_2_plus,-2.105529269,F
+coef_number_of_joint_shopping_tours,-0.6377,F
+coef_number_of_joint_maintenance_tours,-0.4348,F
+coef_number_of_joint_eating_out_tours,-0.7839,F
+coef_number_of_joint_discretionary_tours,-0.085,F
+coef_more_than_one_escorting_tours,2.0794,F
+coef_more_than_one_shopping_tours,-1.1624,F
+coef_more_than_one_maintenance_tours,-0.5943,F
+coef_mediumhigh_income_group_and_shopping_tour,0.4056,F
+coef_high_income_group_and_shopping_tour,0.4056,F
+coef_low_income_group_and_maintenance_tour,-0.2202,F
+coef_mediumlow_income_group_and_maintenance_tour,-0.2202,F
+coef_low_income_group_and_eating_out_tour,-1.0384,F
+coef_medium_low_income_group_and_eating_out_tour,-0.4217,F
+coef_low_income_group_and_discretionary_tour,-0.3758,F
+coef_mediumhigh_income_group_and_discretionary_tour,0.1222,F
+coef_high_income_group_and_discretionary_tour,0.1222,F
+coef_high_income_group_and_visiting_tour,-1.104,F
+coef_female_and_shopping_tour,-0.0455,F
+coef_female_and_maintenance_tour,-0.2926,F
+coef_female_and_eatingout_tour,-0.2472,F
+coef_female_and_discretionary_tour,-0.0647,F
+coef_number_of_full_time_worker_and_escorting_tour,-0.1294,F
+coef_number_of_part_time_worker_and_escorting_tour,-0.1294,F
+coef_number_of_non_worker_and_escorting_tour,-0.1294,F
+coef_number_of_driving_school_kid_and_escorting_tour,0.6054,F
+coef_go_out_pre_driving_school_kid_and_escorting_tour,0.7285,F
+coef_number_of_retiree_and_shopping_tour,-0.4462,F
+coef_number_of_retiree_and_maintenance_tour,-0.4112,F
+coef_number_of_retiree_and_visiting_tour,-0.2327,F
+coef_retired_hh_and_escorting_tour,-0.8133,F
+coef_retired_hh_and_shopping_tour,0.2715,F
+coef_retired_hh_and_eating_out_tour,0.2011,F
+coef_retired_hh_and_discretionary_tour,0.2624,F
+coef_retail_accessibility_for_escorting,0.5109,F
+coef_retail_accessibility_for_shopping,0.2262,F
+coef_retail_accessibility_for_maintenance,0.1154,F
+coef_retail_accessibility_for_discretionary,0.3252,F
+coef_retail_accessibility_for_visiting,0.0533,F
+coef_college_education_for_escorting,0.1522,F
+coef_college_education_for_visiting,0.2179,F
+coef_college_education_for_discretionary,0.3046,F
+coef_college_education_for_shopping,0.2046,F
+coef_low_education_for_discretionary,-0.8504,F
+coef_low_education_for_escorting,-0.8443,F
+coef_low_education_for_shopping,-0.5676,F
+coef_low_education_for_maintenance,-0.6861,F
+coef_detached_household_and_escorting,0.3809,F
+coef_detached_household_and_eating_out,-0.3389,F
+coef_detached_household_and_discretionary,-0.3286,F
+coef_1_escort_tour_constant,-2.342,F
+coef_2_plus_escort_tours_constant,-1.988,F
+coef_1_shopping_tours_constant,-2.414,F
+coef_2_plus_shopping_tours_constant,-2.101,F
+coef_1_maintenance_tours_constant,-2.869,F
+coef_2_plus_maintenance_tours_constant,-3.015,F
+coef_1_plus_eating_out_tours_constant,-2.979,F
+coef_1_plus_visting_tours_constant,-2.806,F
+coef_1_other_discretionary_tours_constant,-3.196,F
+coef_2_plus_other_discretionary_tours_constant,-3.482,F
+coef_1_escort_tour_constant_tm2,-0.287618769,F
+coef_2_plus_escort_tours_constant_tm2,0.65554552,F
+coef_1_shopping_tours_constant_tm2,-1.385280597,F
+coef_2_plus_shopping_tours_constant_tm2,-1.385280597,F
+coef_1_maintenance_tours_constant_tm2,-1.480017973,F
+coef_2_plus_maintenance_tours_constant_tm2,-1.480017973,F
+coef_1_plus_eating_out_tours_constant_tm2,-1.385672408,F
+coef_1_plus_visting_tours_constant_tm2,-1.218799144,F
+coef_1_other_discretionary_tours_constant_tm2,0.538161829,F
+coef_2_plus_other_discretionary_tours_constant_tm2,0.538161829,F
+coef_non_mandatory_dap_and_1_non_mand_tour,1.152725777,F
+coef_non_mandatory_dap_and_2_non_mand_tour,0.022553605,F
+coef_non_mandatory_dap_and_3_non_mand_tour,6.338227151,F
+coef_non_mandatory_dap_and_4_plus_non_mand_tour,6.237997735,F
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv
new file mode 100644
index 000000000..405ec48a1
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv
@@ -0,0 +1,72 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_escorting_tour,-10.3136,F
+coef_discretionary_tour,-8.0543,F
+coef_shopping_tour,-17.8882,F
+coef_maintenance_tour,-14.5925,F
+coef_visiting_or_social_tour,-2.9744,F
+coef_eating_out_tour,-42.1591,F
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_2,-0.7045,F
+coef_total_number_of_tours_is_3,-0.7045,F
+coef_has_mandatory_tours_and_tour_frequency_is_1,-1.8596,F
+coef_has_mandatory_tours_and_tour_frequency_is_2_plus,-6.1247,F
+coef_has_joint_tours_and_tour_frequency_is_1_plus,-1.0154,F
+coef_number_of_joint_discretionary_tours,-1.2257,F
+coef_more_than_one_escorting_tours,0.3358,F
+coef_more_than_one_discretionary_tours,-0.8724,F
+coef_high_income_group_and_shopping_tour,1.0878,F
+coef_mediumhigh_income_group_and_maintenance_tour,1.0499,F
+coef_high_income_group_and_maintenance_tour,1.0499,F
+coef_low_income_group_and_discretionary_tour,-2.1495,F
+coef_medium_low_income_group_and_discretionary_tour,-0.9706,F
+coef_high_income_group_and_discretionary_tour,0.6655,F
+coef_low_income_group_and_visiting_tour,-1.6227,F
+coef_medium_low_income_group_and_visiting_tour,-1.6227,F
+coef_high_income_group_and_visiting_tour,1.1424,F
+coef_car_surplus_vs_workers_and_number_of_maintenance_tours,0.4721,F
+coef_car_surplus_vs_workers_and_number_of_discretionary_tours,0.1531,F
+coef_go_out_pre_driving_school_kid_and_escorting_tour,0.4372,F
+coef_number_of_driving_school_kid_and_shopping_tour,-0.1631,F
+coef_number_of_pre_driving_school_kid_and_shopping_tour,-0.1631,F
+coef_number_of_pre_school_kid_and_shopping_tour,-0.1631,F
+coef_number_of_driving_school_kid_and_visiting_tour,0.3656,F
+coef_number_of_pre_driving_school_kid_and_visiting_tour,0.3656,F
+coef_number_of_pre_school_kid_and_visiting_tour,0.3656,F
+coef_number_of_driving_school_kid_and_discretionary_tour,0.2287,F
+coef_number_of_pre_driving_school_kid_and_discretionary_tour,0.2287,F
+coef_number_of_pre_school_kid_and_discretionary_tour,0.2287,F
+coef_school_accessibility_and_tour_frequency_is_1,0.6512,F
+coef_school_accessibility_and_tour_frequency_is_2_plus,3.8532,F
+coef_retail_accessibility_for_escorting,0.5583,F
+coef_retail_accessibility_for_shopping,0.9818,F
+coef_retail_accessibility_for_maintenance,0.8866,F
+coef_retail_accessibility_for_eating_out,2.8733,F
+coef_retail_accessibility_for_discretionary,0.5082,F
+coef_1_escort_tour_constant,-0.405,F
+coef_2_plus_escort_tours_constant,-0.688,F
+coef_1_shopping_tours_constant,1.708,F
+coef_2_plus_shopping_tours_constant,0,T
+coef_1_maintenance_tours_constant,-1.042,F
+coef_2_plus_maintenance_tours_constant,0,T
+coef_1_plus_eating_out_tours_constant,-3.915,F
+coef_1_plus_visting_tours_constant,-0.653,F
+coef_1_other_discretionary_tours_constant,-1.095,F
+coef_2_plus_other_discretionary_tours_constant,-2.257,F
+coef_1_escort_tour_constant_tm2,-0.491,F
+coef_2_plus_escort_tours_constant_tm2,-0.491,F
+coef_1_shopping_tours_constant_tm2,0.246,F
+coef_2_plus_shopping_tours_constant_tm2,0.246,F
+coef_1_maintenance_tours_constant_tm2,1.445,F
+coef_2_plus_maintenance_tours_constant_tm2,1.445,F
+coef_1_plus_eating_out_tours_constant_tm2,0.005,F
+coef_1_plus_visting_tours_constant_tm2,-2.381,F
+coef_1_other_discretionary_tours_constant_tm2,0.614,F
+coef_2_plus_other_discretionary_tours_constant_tm2,0.614,F
+coef_mandatory_dap_and_1_non_mand_tour,2.692763095,F
+coef_mandatory_dap_and_2_non_mand_tour,5.492779247,F
+coef_mandatory_dap_and_3_plus_non_mand_tour,1.891501612,F
+coef_non_mandatory_dap_and_1_non_mand_tour,4.002447263,F
+coef_non_mandatory_dap_and_2_non_mand_tour,2.426047336,F
+coef_non_mandatory_dap_and_3_non_mand_tour,-5.579294246,F
+coef_non_mandatory_dap_and_4_plus_non_mand_tour,-3.501958214,F
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv
new file mode 100644
index 000000000..8c8809347
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv
@@ -0,0 +1,81 @@
+coefficient_name,value,constrain
+coef_unavailable,-999,T
+coef_escorting_tour,-3.151460752,F
+coef_discretionary_tour,-4.548188518,F
+coef_shopping_tour,-2.772689119,F
+coef_maintenance_tour,-2.262570738,F
+coef_visiting_or_social_tour,-4.212581954,F
+coef_eating_out_tour,-14.69186496,F
+coef_total_number_of_tours_is_0_prior_tours,0,T
+coef_total_number_of_tours_is_1,-0.257343536,F
+coef_has_mandatory_tours_and_tour_frequency_is_2_plus,-1.072062082,F
+coef_has_joint_tours_and_tour_frequency_is_1,-0.336245855,F
+coef_has_joint_tours_and_tour_frequency_is_2_plus,-0.388633303,F
+coef_more_than_one_escorting_tours,2.72245229,F
+coef_low_income_group_and_escorting_tour,0.202123266,F
+coef_medium_low_income_group_and_escorting_tour,0.202123266,F
+coef_medium_high_income_group_and_escorting_tour,-0.060806947,F
+coef_high_income_group_and_escorting_tour,-0.060806947,F
+coef_medium_high_income_group_and_shopping_tour,0.685514685,F
+coef_high_income_group_and_shopping_tour,0.702798296,F
+coef_mediumhigh_income_group_and_maintenance_tour,-0.252582535,F
+coef_high_income_group_and_maintenance_tour,-0.252582535,F
+coef_medium_high_income_group_and_eating_out_tour,1.584951486,F
+coef_high_income_group_and_eating_out_tour,1.584951486,F
+coef_low_income_group_and_discretionary_tour,-0.174583696,F
+coef_mediumhigh_income_group_and_discretionary_tour,0.61551534,F
+coef_high_income_group_and_discretionary_tour,0.61551534,F
+coef_low_income_group_and_visiting_tour,0.709390489,F
+coef_medium_low_income_group_and_visiting_tour,0.709390489,F
+coef_high_income_group_and_visiting_tour,-0.174583696,F
+coef_female_and_escorting_tour,0.187876356,F
+coef_female_and_shopping_tour,0.986977296,F
+coef_female_and_maintenance_tour,0.203259744,F
+coef_female_and_visit_tour,1.063972932,F
+coef_car_surplus_vs_workers_and_tour_frequency_is_1_plus,0.427064876,F
+coef_number_of_full_time_worker_and_escorting_tour,-0.30107932,F
+coef_number_of_part_time_worker_and_escorting_tour,-0.457600247,F
+coef_number_of_non_worker_and_escorting_tour,-0.457600247,F
+coef_number_of_retiree_and_escorting_tour,-0.637017887,F
+coef_number_of_university_student_and_escorting_tour,0.420147909,F
+coef_go_out_pre_driving_school_kid_and_escorting_tour,0.767818174,F
+coef_go_out_school_kid_and_escorting_tour,0.578638577,F
+coef_number_of_full_time_worker_and_shopping_tour,-0.500867474,F
+coef_number_of_part_time_worker_and_shopping_tour,-0.760659628,F
+coef_number_of_university_student_and_shopping_tour,-1.440951366,F
+coef_number_of_full_time_worker_and_maintenance_tour,-0.540708813,F
+coef_number_of_part_time_worker_and_maintenance_tour,-0.540708813,F
+coef_number_of_non_worker_and_maintenance_tour,-0.540708813,F
+coef_number_of_university_student_and_maintenance_tour,-1.047970628,F
+coef_number_of_university_student_and_discretionary_tour,0.089391871,F
+coef_school_accessibility_and_tour_frequency_is_1_plus,0.512981401,F
+coef_retail_accessibility_for_discretionary,0.146060018,F
+coef_walk_accessibility_for_eatout,1.002231311,F
+coef_detached_household_and_discretionary,-0.330258504,F
+coef_1_escort_tour_constant,0.212419359,F
+coef_2_plus_escort_tours_constant,0.941880945,F
+coef_1_shopping_tours_constant,0.292005818,F
+coef_2_plus_shopping_tours_constant,0.365107316,F
+coef_1_maintenance_tours_constant,-0.361543976,F
+coef_2_plus_maintenance_tours_constant,0,T
+coef_1_plus_eating_out_tours_constant,-0.297763109,F
+coef_1_plus_visting_tours_constant,-0.657474419,F
+coef_1_other_discretionary_tours_constant,0.941880945,F
+coef_2_plus_other_discretionary_tours_constant,-0.390350079,F
+coef_1_escort_tour_constant_tm2,-0.367737876,F
+coef_2_plus_escort_tours_constant_tm2,-1.248352386,F
+coef_1_shopping_tours_constant_tm2,-1.437353532,F
+coef_2_plus_shopping_tours_constant_tm2,-1.437353532,F
+coef_1_maintenance_tours_constant_tm2,-1.454556703,F
+coef_2_plus_maintenance_tours_constant_tm2,-1.454556703,F
+coef_1_plus_eating_out_tours_constant_tm2,1.506897479,F
+coef_1_plus_visting_tours_constant_tm2,-0.716560806,F
+coef_1_other_discretionary_tours_constant_tm2,-0.194927725,F
+coef_2_plus_other_discretionary_tours_constant_tm2,-0.194927725,F
+coef_mandatory_dap_and_1_non_mand_tour,0.775208618,F
+coef_mandatory_dap_and_2_non_mand_tour,1.056946603,F
+coef_mandatory_dap_and_3_plus_non_mand_tour,-5.001269397,F
+coef_non_mandatory_dap_and_1_non_mand_tour,-0.396651853,F
+coef_non_mandatory_dap_and_2_non_mand_tour,-0.794997137,F
+coef_non_mandatory_dap_and_3_non_mand_tour,-2.141825245,F
+coef_non_mandatory_dap_and_4_plus_non_mand_tour,-5.009271306,F
diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_extension_probs.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_extension_probs.csv
new file mode 100644
index 000000000..830885237
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_extension_probs.csv
@@ -0,0 +1,193 @@
+ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours
+1,0,0,1,0.71087271,0.886861358,1
+2,0,0,1,0.640712953,0.863555773,1
+3,0,0,1,0.726456335,0.726456335,1
+4,0,0,1,0.665679012,0.903816044,1
+5,0,0,1,0.861430335,0.92284115,1
+6,0,0,1,1,1,1
+7,0,0,1,0,1,1
+8,0,0,1,0.754632385,0.918132897,1
+1,1,0,1,0.824975545,1,1
+2,1,0,1,0.881285721,0.964131273,1
+3,1,0,1,0.856158692,1,1
+4,1,0,1,1,1,1
+5,1,0,1,1,1,1
+6,1,0,1,1,1,1
+7,1,0,1,1,1,1
+8,1,0,1,1,1,1
+1,0,0,2,0.918802841,1,1
+2,0,0,2,1,1,1
+3,0,0,2,1,1,1
+4,0,0,2,1,1,1
+5,0,0,2,0.911351921,0.964395496,1
+6,0,0,2,1,1,1
+7,0,0,2,1,1,1
+8,0,0,2,0.91707294,1,1
+1,1,0,2,1,1,1
+2,1,0,2,1,1,1
+3,1,0,2,1,1,1
+4,1,0,2,1,1,1
+5,1,0,2,1,1,1
+6,1,0,2,1,1,1
+7,1,0,2,1,1,1
+8,1,0,2,1,1,1
+1,0,0,3,0.885080064,0.942540032,1
+2,0,0,3,0.832615585,1,1
+3,0,0,3,1,1,1
+4,0,0,3,1,1,1
+5,0,0,3,0.874364173,0.972270724,1
+6,0,0,3,1,1,1
+7,0,0,3,1,1,1
+8,0,0,3,1,1,1
+1,1,0,3,1,1,1
+2,1,0,3,1,1,1
+3,1,0,3,1,1,1
+4,1,0,3,1,1,1
+5,1,0,3,1,1,1
+6,1,0,3,1,1,1
+7,1,0,3,1,1,1
+8,1,0,3,1,1,1
+1,0,0,4,1,1,1
+2,0,0,4,0.99296599,1,1
+3,0,0,4,1,1,1
+4,0,0,4,0.976072814,1,1
+5,0,0,4,0.956476086,0.993572522,1
+6,0,0,4,1,1,1
+7,0,0,4,1,1,1
+8,0,0,4,0.991144007,1,1
+1,1,0,4,0.99740908,1,1
+2,1,0,4,0.939184617,1,1
+3,1,0,4,1,1,1
+4,1,0,4,0.749851433,1,1
+5,1,0,4,1,1,1
+6,1,0,4,1,1,1
+7,1,0,4,1,1,1
+8,1,0,4,0.949874016,1,1
+1,0,0,5,1,1,1
+2,0,0,5,1,1,1
+3,0,0,5,1,1,1
+4,0,0,5,0.954541082,1,1
+5,0,0,5,0.955216408,1,1
+6,0,0,5,1,1,1
+7,0,0,5,1,1,1
+8,0,0,5,0.931183243,1,1
+1,1,0,5,0.970742777,1,1
+2,1,0,5,0.882901912,1,1
+3,1,0,5,1,1,1
+4,1,0,5,0.334132623,1,1
+5,1,0,5,1,1,1
+6,1,0,5,0.934804611,0.934804611,1
+7,1,0,5,0.965932685,1,1
+8,1,0,5,1,1,1
+1,0,0,6,1,1,1
+2,0,0,6,1,1,1
+3,0,0,6,1,1,1
+4,0,0,6,0.86514867,1,1
+5,0,0,6,0.896321835,1,1
+6,0,0,6,1,1,1
+7,0,0,6,1,1,1
+8,0,0,6,1,1,1
+1,1,0,6,1,1,1
+2,1,0,6,1,1,1
+3,1,0,6,1,1,1
+4,1,0,6,1,1,1
+5,1,0,6,1,1,1
+6,1,0,6,1,1,1
+7,1,0,6,1,1,1
+8,1,0,6,1,1,1
+1,0,1,1,1,1,1
+2,0,1,1,1,1,1
+3,0,1,1,1,1,1
+4,0,1,1,1,1,1
+5,0,1,1,1,1,1
+6,0,1,1,1,1,1
+7,0,1,1,1,1,1
+8,0,1,1,1,1,1
+1,1,1,1,1,1,1
+2,1,1,1,1,1,1
+3,1,1,1,1,1,1
+4,1,1,1,1,1,1
+5,1,1,1,1,1,1
+6,1,1,1,1,1,1
+7,1,1,1,1,1,1
+8,1,1,1,1,1,1
+1,0,1,2,1,1,1
+2,0,1,2,1,1,1
+3,0,1,2,1,1,1
+4,0,1,2,1,1,1
+5,0,1,2,1,1,1
+6,0,1,2,1,1,1
+7,0,1,2,1,1,1
+8,0,1,2,1,1,1
+1,1,1,2,1,1,1
+2,1,1,2,1,1,1
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+6,1,1,2,1,1,1
+7,1,1,2,1,1,1
+8,1,1,2,1,1,1
+1,0,1,3,1,1,1
+2,0,1,3,1,1,1
+3,0,1,3,1,1,1
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+5,0,1,3,1,1,1
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+7,0,1,3,1,1,1
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+8,1,1,3,1,1,1
+1,0,1,4,1,1,1
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+7,0,1,4,1,1,1
+8,0,1,4,1,1,1
+1,1,1,4,1,1,1
+2,1,1,4,1,1,1
+3,1,1,4,1,1,1
+4,1,1,4,1,1,1
+5,1,1,4,1,1,1
+6,1,1,4,1,1,1
+7,1,1,4,1,1,1
+8,1,1,4,1,1,1
+1,0,1,5,1,1,1
+2,0,1,5,1,1,1
+3,0,1,5,1,1,1
+4,0,1,5,1,1,1
+5,0,1,5,1,1,1
+6,0,1,5,1,1,1
+7,0,1,5,1,1,1
+8,0,1,5,1,1,1
+1,1,1,5,1,1,1
+2,1,1,5,1,1,1
+3,1,1,5,1,1,1
+4,1,1,5,1,1,1
+5,1,1,5,1,1,1
+6,1,1,5,1,1,1
+7,1,1,5,1,1,1
+8,1,1,5,1,1,1
+1,0,1,6,1,1,1
+2,0,1,6,1,1,1
+3,0,1,6,1,1,1
+4,0,1,6,1,1,1
+5,0,1,6,1,1,1
+6,0,1,6,1,1,1
+7,0,1,6,1,1,1
+8,0,1,6,1,1,1
+1,1,1,6,1,1,1
+2,1,1,6,1,1,1
+3,1,1,6,1,1,1
+4,1,1,6,1,1,1
+5,1,1,6,1,1,1
+6,1,1,6,1,1,1
+7,1,1,6,1,1,1
+8,1,1,6,1,1,1
diff --git a/test/non_mandatory_tour_frequency/configs/settings.yaml b/test/non_mandatory_tour_frequency/configs/settings.yaml
new file mode 100644
index 000000000..4136d0b80
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/settings.yaml
@@ -0,0 +1,60 @@
+# input tables
+input_table_list:
+  - tablename: households
+    filename: households.csv
+    index_col: household_id
+    rename_columns:
+      unique_hh_id: household_id
+      NP: hhsize
+      hh_workers_from_esr: num_workers
+      VEH: auto_ownership
+      MAZ: home_zone_id
+      HINCP: income
+    keep_columns:
+      - home_zone_id
+      - income
+      - hhsize
+      - HHT
+      - auto_ownership
+      - num_workers
+  - tablename: persons
+    filename: persons.csv
+    index_col: person_id
+    rename_columns:
+      unique_hh_id: household_id
+      AGEP: age
+      SPORDER: PNUM
+      SEX: sex
+      employed: pemploy
+      student_status: pstudent
+      person_type: ptype
+    keep_columns:
+      - household_id
+      - age
+      - PNUM
+      - sex
+      - pemploy
+      - pstudent
+      - ptype
+  - tablename: land_use
+    filename: land_use.csv
+    index_col: zone_id
+    rename_columns:
+      MAZ_ORIGINAL: zone_id
+      CountyID: county_id
+      TAZ_ORIGINAL: TAZ
+      DistID: DISTRICT
+      HH: TOTHH
+      POP: TOTPOP
+      ACRES: TOTACRE
+      emp_total: TOTEMP
+    keep_columns:
+      - TAZ
+      - DISTRICT
+      - SD
+      - county_id
+      - TOTHH
+      - TOTPOP
+      - TOTACRE
+
+trace_hh_id: 
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/configs/tour_departure_and_duration_alternatives.csv b/test/non_mandatory_tour_frequency/configs/tour_departure_and_duration_alternatives.csv
new file mode 100644
index 000000000..bddab06b9
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/configs/tour_departure_and_duration_alternatives.csv
@@ -0,0 +1,191 @@
+start,end
+5,5
+5,6
+5,7
+5,8
+5,9
+5,10
+5,11
+5,12
+5,13
+5,14
+5,15
+5,16
+5,17
+5,18
+5,19
+5,20
+5,21
+5,22
+5,23
+6,6
+6,7
+6,8
+6,9
+6,10
+6,11
+6,12
+6,13
+6,14
+6,15
+6,16
+6,17
+6,18
+6,19
+6,20
+6,21
+6,22
+6,23
+7,7
+7,8
+7,9
+7,10
+7,11
+7,12
+7,13
+7,14
+7,15
+7,16
+7,17
+7,18
+7,19
+7,20
+7,21
+7,22
+7,23
+8,8
+8,9
+8,10
+8,11
+8,12
+8,13
+8,14
+8,15
+8,16
+8,17
+8,18
+8,19
+8,20
+8,21
+8,22
+8,23
+9,9
+9,10
+9,11
+9,12
+9,13
+9,14
+9,15
+9,16
+9,17
+9,18
+9,19
+9,20
+9,21
+9,22
+9,23
+10,10
+10,11
+10,12
+10,13
+10,14
+10,15
+10,16
+10,17
+10,18
+10,19
+10,20
+10,21
+10,22
+10,23
+11,11
+11,12
+11,13
+11,14
+11,15
+11,16
+11,17
+11,18
+11,19
+11,20
+11,21
+11,22
+11,23
+12,12
+12,13
+12,14
+12,15
+12,16
+12,17
+12,18
+12,19
+12,20
+12,21
+12,22
+12,23
+13,13
+13,14
+13,15
+13,16
+13,17
+13,18
+13,19
+13,20
+13,21
+13,22
+13,23
+14,14
+14,15
+14,16
+14,17
+14,18
+14,19
+14,20
+14,21
+14,22
+14,23
+15,15
+15,16
+15,17
+15,18
+15,19
+15,20
+15,21
+15,22
+15,23
+16,16
+16,17
+16,18
+16,19
+16,20
+16,21
+16,22
+16,23
+17,17
+17,18
+17,19
+17,20
+17,21
+17,22
+17,23
+18,18
+18,19
+18,20
+18,21
+18,22
+18,23
+19,19
+19,20
+19,21
+19,22
+19,23
+20,20
+20,21
+20,22
+20,23
+21,21
+21,22
+21,23
+22,22
+22,23
+23,23
\ No newline at end of file
diff --git a/test/non_mandatory_tour_frequency/data/.gitkeep b/test/non_mandatory_tour_frequency/data/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/non_mandatory_tour_frequency/test_non_mandatory_tour_frequency.py b/test/non_mandatory_tour_frequency/test_non_mandatory_tour_frequency.py
new file mode 100644
index 000000000..1563de756
--- /dev/null
+++ b/test/non_mandatory_tour_frequency/test_non_mandatory_tour_frequency.py
@@ -0,0 +1,336 @@
+import logging
+import pytest
+import os
+import shutil
+import pandas as pd
+import numpy as np
+from numpy import dot
+from numpy.linalg import norm
+
+# import models is necessary to initalize the model steps with orca
+from activitysim.abm import models
+from activitysim.core import pipeline, config
+from activitysim.core import tracing
+
+logger = logging.getLogger(__name__)
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def module() -> str:
+    """
+    A pytest fixture that returns the data folder location.
+    :return: folder location for any necessary data to initialize the tests
+    """
+    return "non_mandatory_tour_frequency"
+
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def tables(prepare_module_inputs) -> dict[str, str]:
+    """
+    A pytest fixture that returns the "mock" tables to build pipeline dataframes. The
+    key-value pair is the name of the table and the index column.
+    :return: dict
+    """
+    return {
+        "land_use": "MAZ_ORIGINAL",
+        "persons": "person_id",
+        "households": "household_id",
+        "accessibility": "MAZ_ORIGINAL",
+        "tours": "tour_id",
+    }
+
+
+# Used by conftest.py initialize_pipeline method
+# Set to true if you need to read skims into the pipeline
+@pytest.fixture(scope="module")
+def initialize_network_los() -> bool:
+    """
+    A pytest boolean fixture indicating whether network skims should be read from the
+    fixtures test data folder.
+    :return: bool
+    """
+    return False
+
+
+@pytest.fixture(scope="module")
+def load_checkpoint() -> bool:
+    """
+    checkpoint to be loaded from the pipeline when reconnecting.
+    """
+    return "initialize_households"
+
+
+# make a reconnect_pipeline internal to test module
+@pytest.mark.skipif(
+    os.path.isfile("test/non_mandatory_tour_frequency/output/pipeline.h5"),
+    reason="no need to recreate pipeline store if alreayd exist",
+)
+def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog):
+    # Run summarize model
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(models=["initialize_landuse", "initialize_households"])
+
+    # get the updated pipeline data
+    person_df = pipeline.get_table("persons")
+    person_df.to_csv("test/non_mandatory_tour_frequency/output/person.csv", index=False)
+
+    # get the updated pipeline data
+    household_df = pipeline.get_table("households")
+    household_df.to_csv(
+        "test/non_mandatory_tour_frequency/output/household.csv", index=False
+    )
+
+    pipeline.close_pipeline()
+
+
+def test_nmtf_from_pipeline(reconnect_pipeline: pipeline.Pipeline, caplog):
+    # Run summarize model
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(
+        models=["non_mandatory_tour_frequency"], resume_after="initialize_households"
+    )
+
+    # get the updated pipeline data
+    person_df = pipeline.get_table("persons")
+
+    # get the updated pipeline data
+    household_df = pipeline.get_table("households")
+
+    ############################
+    # person nmtf validation
+    ############################
+    # nmtf person result from the model
+    logger.info("person nmtf pattern validation")
+
+    target_key = "inmf_choice"
+    simulated_key = "non_mandatory_tour_frequency"
+    similarity_threshold = 0.99
+
+    simulated_df = create_summary(
+        person_df, key=simulated_key, out_col="Simulated_Share"
+    )
+
+    # result from the TM2 run
+    target_df = create_summary(person_df, key=target_key, out_col="Target_Share")
+
+    # compare simulated and target results
+    similarity_value = compare_simulated_against_target(
+        target_df, simulated_df, target_key, simulated_key
+    )
+
+    # if the cosine_similarity >= threshold then the simulated and target results are "similar"
+    assert similarity_value >= similarity_threshold
+
+
+# fetch/prepare existing files for model inputs
+# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim
+@pytest.fixture(scope="module")
+def prepare_module_inputs() -> None:
+    """
+    copy input files from sharepoint into test folder
+
+    create unique person id in person file
+
+    :return: None
+    """
+    # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/
+    # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1
+    test_dir = os.path.join("test", "non_mandatory_tour_frequency", "data")
+
+    accessibility_file = os.path.join(test_dir, "tm2_outputs", "accessibilities.csv")
+    household_file = os.path.join(test_dir, "popsyn", "households.csv")
+    person_file = os.path.join(test_dir, "popsyn", "persons.csv")
+    landuse_file = os.path.join(test_dir, "landuse", "maz_data_withDensity.csv")
+
+    shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv"))
+    shutil.copy(household_file, os.path.join(test_dir, "households.csv"))
+    shutil.copy(person_file, os.path.join(test_dir, "persons.csv"))
+    shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv"))
+
+    # add original maz id to accessibility table
+    land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv"))
+    accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv"))
+
+    accessibility_df = pd.merge(
+        accessibility_df,
+        land_use_df[["MAZ", "MAZ_ORIGINAL"]].rename(columns={"MAZ": "mgra"}),
+        how="left",
+        on="mgra",
+    )
+
+    accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False)
+
+    # currently household file has to have these two columns, even before annotation
+    # because annotate person happens before household and uses these two columns
+    # TODO find a way to get around this
+    ####
+    household_df = pd.read_csv(os.path.join(test_dir, "households.csv"))
+
+    household_columns_dict = {"HHID": "household_id", "MAZ": "home_zone_id"}
+
+    household_df.rename(columns=household_columns_dict, inplace=True)
+
+    tm2_simulated_household_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "householdData_1.csv")
+    )
+    tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    household_df = pd.merge(
+        household_df,
+        tm2_simulated_household_df[
+            [
+                "household_id",
+                "autos",
+                "automated_vehicles",
+                "transponder",
+                "cdap_pattern",
+                "jtf_choice",
+            ]
+        ],
+        how="inner",  # tm2 is not 100% sample run
+        on="household_id",
+    )
+
+    household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False)
+
+    person_df = pd.read_csv(os.path.join(test_dir, "persons.csv"))
+
+    person_columns_dict = {"HHID": "household_id", "PERID": "person_id"}
+
+    person_df.rename(columns=person_columns_dict, inplace=True)
+
+    tm2_simulated_person_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "personData_1.csv")
+    )
+    tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    person_df = pd.merge(
+        person_df,
+        tm2_simulated_person_df[
+            [
+                "household_id",
+                "person_id",
+                "person_num",
+                "type",
+                "value_of_time",
+                "activity_pattern",
+                "imf_choice",
+                "inmf_choice",
+                "fp_choice",
+                "reimb_pct",
+                "workDCLogsum",
+                "schoolDCLogsum",
+            ]
+        ],
+        how="inner",  # tm2 is not 100% sample run
+        on=["household_id", "person_id"],
+    )
+
+    # get tm2 simulated workplace and school location results
+    tm2_simulated_wsloc_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "wsLocResults_1.csv")
+    )
+    tm2_simulated_wsloc_df.rename(
+        columns={"HHID": "household_id", "PersonID": "person_id"}, inplace=True
+    )
+
+    person_df = pd.merge(
+        person_df,
+        tm2_simulated_wsloc_df[
+            [
+                "household_id",
+                "person_id",
+                "WorkLocation",
+                "WorkLocationLogsum",  # this is the same as `workDCLogsum` in tm2 person output
+                "SchoolLocation",
+                "SchoolLocationLogsum",  # this is the same as `schoolDCLogsum` in tm2 person output
+            ]
+        ],
+        how="inner",  # ctramp might not be 100% sample run
+        on=["household_id", "person_id"],
+    )
+
+    person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False)
+
+    ## get tour data from tn2 output
+
+    tm2_simulated_indiv_tour_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "indivTourData_1.csv")
+    )
+    tm2_simulated_joint_tour_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "jointTourData_1.csv")
+    )
+
+    tm2_simulated_tour_df = pd.concat(
+        [tm2_simulated_indiv_tour_df, tm2_simulated_joint_tour_df],
+        sort=False,
+        ignore_index=True,
+    )
+
+    tm2_simulated_tour_df.rename(columns={"hh_id": "household_id"}).to_csv(
+        os.path.join(test_dir, "tours.csv"), index=False
+    )
+
+    ####
+
+
+def create_summary(input_df, key, out_col="Share") -> pd.DataFrame:
+    """
+    Create summary for the input data.
+    1. group input data by the "key" column
+    2. calculate the percent of input data records in each "key" category.
+
+    :return: pd.DataFrame
+    """
+
+    out_df = input_df.groupby(key).size().reset_index(name="Count")
+    out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4)
+
+    return out_df[[key, out_col]]
+
+
+def cosine_similarity(a, b):
+    """
+    Computes cosine similarity between two vectors.
+
+    Cosine similarity is used here as a metric to measure similarity between two sequence of numbers.
+    Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them
+    i.e., dot products of the vectors divided by the product of their lengths.
+
+    :return:
+    """
+
+    return dot(a, b) / (norm(a) * norm(b))
+
+
+def compare_simulated_against_target(
+    target_df: pd.DataFrame,
+    simulated_df: pd.DataFrame,
+    target_key: str,
+    simulated_key: str,
+) -> bool:
+    """
+    compares the simulated and target results by computing the cosine similarity between them.
+
+    :return:
+    """
+
+    merged_df = pd.merge(
+        target_df, simulated_df, left_on=target_key, right_on=simulated_key, how="outer"
+    )
+    merged_df = merged_df.fillna(0)
+
+    logger.info("simulated vs target share:\n%s" % merged_df)
+
+    similarity_value = cosine_similarity(
+        merged_df["Target_Share"].tolist(), merged_df["Simulated_Share"].tolist()
+    )
+
+    logger.info("cosine similarity:\n%s" % similarity_value)
+
+    return similarity_value
diff --git a/test/parking_location/.gitkeep b/test/parking_location/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/parking_location/compare-parking-location-results.R b/test/parking_location/compare-parking-location-results.R
new file mode 100644
index 000000000..22c607f8d
--- /dev/null
+++ b/test/parking_location/compare-parking-location-results.R
@@ -0,0 +1,62 @@
+library(tidyverse)
+library(ggplot2)
+
+trips_df <- read_csv("./output/trips_after_parking_choice.csv")
+
+walk_dist_df <- read_csv("./data/ped_distance_maz_maz.csv")
+
+landuse_df <- read_csv("./data/land_use.csv")
+
+landuse_df <- landuse_df %>%
+  select(maz, maz_county_based, TAZ_ORIGINAL)
+
+work_df <- trips_df %>%
+  rename(parking_maz_target = parking_mgra) %>%
+  rename(parking_maz_simulated = parking_zone) %>%
+  select(trip_id, household_id, person_id, person_num, stop_id, tour_purpose, orig_purpose, dest_purpose, orig_maz = origin, dest_maz = destination, 
+         activity_duration_in_hours, parking_maz_target, parking_maz_simulated) %>%
+  left_join(landuse_df, by = c("orig_maz" = "maz")) %>%
+  rename(orig_maz_county_based = maz_county_based, orig_taz = TAZ_ORIGINAL) %>%
+  left_join(landuse_df, by = c("dest_maz" = "maz")) %>%
+  rename(dest_maz_county_based = maz_county_based, dest_taz = TAZ_ORIGINAL) %>%
+  left_join(landuse_df, by = c("parking_maz_target" = "maz")) %>%
+  rename(parking_maz_county_based_target = maz_county_based, parking_taz_target = TAZ_ORIGINAL) %>%
+  left_join(landuse_df, by = c("parking_maz_simulated" = "maz")) %>%
+  rename(parking_maz_county_based_simulated = maz_county_based, parking_taz_simulated = TAZ_ORIGINAL) %>%
+  left_join(walk_dist_df, by = c("parking_maz_simulated" = "OMAZ", "dest_maz" = "DMAZ")) %>%
+  rename(parking_distance_simulated = DISTWALK) %>%
+  left_join(walk_dist_df, by = c("parking_maz_target" = "OMAZ", "dest_maz" = "DMAZ")) %>%
+  rename(parking_distance_target = DISTWALK) %>%
+  left_join(walk_dist_df, by = c("parking_maz_target" = "OMAZ", "parking_maz_simulated" = "DMAZ")) %>%
+  rename(distance_target_simulation = DISTWALK) %>%
+  select(trip_id, household_id, person_id, stop_id, tour_purpose, orig_purpose, dest_purpose,
+         orig_maz, dest_maz, orig_maz_county_based, dest_maz_county_based, orig_taz, dest_taz,
+         parking_maz_target, parking_maz_simulated, parking_taz_target, parking_taz_simulated,
+         parking_distance_target, parking_distance_simulated, distance_target_simulation)
+
+
+write.csv(work_df, "./output/parking-location-choice-results.csv", row.names = F)
+
+
+summary_df <- work_df %>%
+  group_by(dest_purpose) %>%
+  summarise(
+    trips_count = n(),
+    min_distance_target = round(min(parking_distance_target, na.rm = T), 3),
+    min_distance_simulated = round(min(parking_distance_simulated, na.rm = T), 3),
+    max_distance_target = round(max(parking_distance_target, na.rm = T), 3),
+    max_distance_simulated = round(max(parking_distance_simulated, na.rm = T), 3),
+    mean_distance_target = round(mean(parking_distance_target, na.rm = T), 3),
+    mean_distance_simulated = round(mean(parking_distance_simulated, na.rm = T), 3),
+    median_distance_target = round(median(parking_distance_target, na.rm = T), 3),
+    median_distance_simulated = round(median(parking_distance_simulated, na.rm = T), 3)
+  )
+
+#ggplot(work_df, aes(x=distance_target_simulation)) + geom_histogram(binwidth=0.025, color="black", fill="white")
+
+#ggplot(work_df, aes(x=parking_distance_target)) + geom_histogram(binwidth=0.05, color="black", fill="white")
+
+#ggplot(work_df, aes(x=parking_distance_simulated)) + geom_histogram(binwidth=0.05, color="black", fill="white")
+
+
+
diff --git a/test/parking_location/configs/ParkLocationChoice.xls b/test/parking_location/configs/ParkLocationChoice.xls
new file mode 100644
index 000000000..3ea952910
Binary files /dev/null and b/test/parking_location/configs/ParkLocationChoice.xls differ
diff --git a/test/parking_location/configs/annotate_households.csv b/test/parking_location/configs/annotate_households.csv
new file mode 100644
index 000000000..ac21d3797
--- /dev/null
+++ b/test/parking_location/configs/annotate_households.csv
@@ -0,0 +1,13 @@
+Description,Target,Expression
+#,, annotate households table after import
+,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
+number of fulltime workers,num_full_time_workers,"_PERSON_COUNT('ptype == 1', persons, households)"
+number of parttime workers,num_part_time_workers,"_PERSON_COUNT('ptype == 2', persons, households)"
+number of university student,num_university_students,"_PERSON_COUNT('ptype == 3', persons, households)"
+number of non-workers,num_non_workers,"_PERSON_COUNT('ptype == 4', persons, households)"
+number of retirees,num_retirees,"_PERSON_COUNT('ptype == 5', persons, households)"
+number of driving age students,num_driving_age_students,"_PERSON_COUNT('ptype == 6', persons, households)"
+number of pre-driving age school kids,num_pre_driving_age_school_kids,"_PERSON_COUNT('ptype == 7', persons, households)"
+number of pre-school kids,num_pre_school_kids,"_PERSON_COUNT('ptype == 8', persons, households)"
+number of pre-driving age school kids who go out,num_pre_driving_age_school_kids_go_out,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons, households)"
+number of pre-school kids who go out,num_pre_school_kids_go_out,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons, households)"
\ No newline at end of file
diff --git a/test/parking_location/configs/annotate_landuse.csv b/test/parking_location/configs/annotate_landuse.csv
new file mode 100644
index 000000000..76c722914
--- /dev/null
+++ b/test/parking_location/configs/annotate_landuse.csv
@@ -0,0 +1,7 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+household_density,household_density,land_use.TOTHH / land_use.TOTACRE
+employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE
+population_density,population_density,land_use.TOTPOP / land_use.TOTACRE
+density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
+is_parking_zone,is_parking_zone,"np.where(land_use.parkarea==1,True,False)"
\ No newline at end of file
diff --git a/test/parking_location/configs/annotate_persons.csv b/test/parking_location/configs/annotate_persons.csv
new file mode 100644
index 000000000..eff11ab68
--- /dev/null
+++ b/test/parking_location/configs/annotate_persons.csv
@@ -0,0 +1,16 @@
+Description,Target,Expression
+#,, annotate persons table after import
+age_16_to_19,age_16_to_19,"persons.age.between(16, 19)"
+age_16_p,age_16_p,persons.age >= 16
+adult,adult,persons.age >= 18
+male,male,persons.sex == 1
+female,female,persons.sex == 2
+,ptype,0
+,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)"
+,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)"
+,ptype,"np.where(persons.type == 'University student', 3, ptype)"
+,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)"
+,ptype,"np.where(persons.type == 'Retired', 5, ptype)"
+,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)"
+,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)"
+,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)"
\ No newline at end of file
diff --git a/test/parking_location/configs/annotate_persons_after_hh.csv b/test/parking_location/configs/annotate_persons_after_hh.csv
new file mode 100644
index 000000000..0dfa16be6
--- /dev/null
+++ b/test/parking_location/configs/annotate_persons_after_hh.csv
@@ -0,0 +1,5 @@
+Description,Target,Expression
+#,, annotate persons table after annotate_households
+#,, adults get full hh_value_of_time and children get 60%
+,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)"
+,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)"
\ No newline at end of file
diff --git a/test/parking_location/configs/initialize_households.yaml b/test/parking_location/configs/initialize_households.yaml
new file mode 100644
index 000000000..15891431e
--- /dev/null
+++ b/test/parking_location/configs/initialize_households.yaml
@@ -0,0 +1,37 @@
+annotate_tables:
+  - tablename: persons
+    column_map:
+      HHID: household_id
+      PERID: person_id
+      AGEP: age
+      SEX: sex
+      SCHL: education_attainment
+      OCCP: occupation
+      WKHP: hours_worked
+      WKW: weeks_worked
+      EMPLOYED: employment_status
+      ESR: esr
+      SCHG: grade_attending
+      activity_pattern: cdap_activity
+    annotate:
+      SPEC: annotate_persons
+      DF: persons
+      TABLES:
+        - households
+  - tablename: households
+    column_map:
+      HHID: household_id
+      HHINCADJ: income
+      NWRKRS_ESR: num_workers
+      VEH: auto_ownership
+      NP: hhsize
+      HHT: hh_type
+      BLD: building_size
+      TYPE: hh_unit_type
+      MTCCountyID: county_id
+    annotate:
+      SPEC: annotate_households
+      DF: households
+      TABLES:
+        - persons
+        - land_use
\ No newline at end of file
diff --git a/test/parking_location/configs/initialize_landuse.yaml b/test/parking_location/configs/initialize_landuse.yaml
new file mode 100644
index 000000000..d30ddb245
--- /dev/null
+++ b/test/parking_location/configs/initialize_landuse.yaml
@@ -0,0 +1,52 @@
+annotate_tables:
+  - tablename: land_use
+    column_map:
+        CountyID: county_id
+        DistID: DISTRICT
+        HH: TOTHH
+        POP: TOTPOP
+        ACRES: TOTACRE
+        emp_total: TOTEMP
+    annotate:
+      SPEC: annotate_landuse
+      DF: land_use
+  - tablename: accessibility
+    column_map:
+        column_1: nonmandatory_auto_accessibility
+        column_2: nonmandatory_transit_accessibility
+        column_3: nonmandatory_nm_accessibility
+        column_4: nonmandatory_sov0_accessibility
+        column_5: nonmandatory_sov1_accessibility
+        column_6: nonmandatory_sov2_accessibility
+        column_7: nonmandatory_hov0_accessibility
+        column_8: nonmandatory_hov1_accessibility
+        column_9: nonmandatory_hov2_accessibility
+        column_10: shop_hov_insufficient_accessibility
+        column_11: shop_hov_sufficient_accessibility
+        column_12: shop_hov_oversufficient_accessibility
+        column_13: maint_hov_insufficient_accessibility
+        column_14: maint_hov_sufficient_accessibility
+        column_15: maint_hov_oversufficient_accessibility
+        column_16: eat_hov_insufficient_accessibility
+        column_17: eat_hov_sufficient_accessibility
+        column_18: eat_hov_oversufficient_accessibility
+        column_19: visit_hov_insufficient_accessibility
+        column_20: visit_hov_sufficient_accessibility
+        column_21: visit_hov_oversufficient_accessibility
+        column_22: discr_hov_insufficient_accessibility
+        column_23: discr_hov_sufficient_accessibility
+        column_24: discr_hov_oversufficient_accessibility
+        column_25: escort_hov_insufficient_accessibility
+        column_26: escort_hov_sufficient_accessibility
+        column_27: escort_hov_oversufficient_accessibility
+        column_28: shop_sov_insufficient_accessibility
+        column_29: shop_sov_sufficient_accessibility
+        column_30: shop_sov_oversufficient_accessibility
+        column_31: maint_sov_insufficient_accessibility
+        column_32: maint_sov_sufficient_accessibility
+        column_33: maint_sov_oversufficient_accessibility
+        column_40: discr_sov_insufficient_accessibility
+        column_41: discr_sov_sufficient_accessibility
+        column_42: discr_sov_oversufficient_accessibility
+        column_45: total_emp_accessibility
+        column_47: hh_walktransit_accessibility
\ No newline at end of file
diff --git a/test/parking_location/configs/network_los.yaml b/test/parking_location/configs/network_los.yaml
new file mode 100644
index 000000000..5603ab859
--- /dev/null
+++ b/test/parking_location/configs/network_los.yaml
@@ -0,0 +1,24 @@
+# read cached skims (using numpy memmap) from output directory (memmap is faster than omx )
+read_skim_cache: False
+# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs
+write_skim_cache: False
+
+zone_system: 2
+
+taz_skims: skims.omx
+
+maz: maz.csv
+
+maz_to_maz:
+  tables:
+    - ped_distance_maz_maz.csv
+
+  # maz_to_maz blending distance (missing or 0 means no blending)
+  max_blend_distance:
+    DISTWALK: 0
+
+skim_time_periods:
+    time_window: 1440
+    period_minutes: 60
+    periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59
+    labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV']
\ No newline at end of file
diff --git a/test/parking_location/configs/parking_location_choice.csv b/test/parking_location/configs/parking_location_choice.csv
new file mode 100644
index 000000000..d9a10c39d
--- /dev/null
+++ b/test/parking_location/configs/parking_location_choice.csv
@@ -0,0 +1,34 @@
+Description,Expression,no_segmentation
+Person is worker,_person_is_worker@((df.ptype == 1) | (df.ptype == 2)),1
+Trip is work purpose,_work_trip@(df.purpose == 'work'),1
+Number of hourly stalls,_hourly_stalls@df.hstallsoth,1
+Number of daily stalls,_daily_stalls@df.dstallsoth,1
+Number of monthly stalls,_monthly_stalls@df.mstallsoth,1
+Hourly stalls available,_hourly_stalls_available@_hourly_stalls > 0,1
+Daily stalls available,_daily_stalls_available@_daily_stalls > 0,1
+Monthly stalls available,_monthly_stalls_available@_monthly_stalls > 0,1
+Hourly parking cost cents,_hourly_parking_cost@df.hparkcost * 100,1
+Number of free parking hours,_num_free_hours@df.numfreehrs,1
+Daily parking cost cents,_daily_parking_cost@df.dparkcost * 100,1
+Monthly parking cost cents,_monthly_parking_cost@df.mparkcost * 100,1
+Monthly parking is allowed,_monthly_allowed@_monthly_stalls_available & _work_trip & _person_is_worker,1
+Total hourly parking cost,_total_hourly_parking_cost@(_hourly_parking_cost * (df.activity_duration - _num_free_hours)),1
+Hourly is cheaper than daily,"_hourly_cheaper_than_daily@np.where(_daily_stalls_available, _total_hourly_parking_cost < _daily_parking_cost,True)",1
+Hourly is cheaper than monthly,"_hourly_cheaper_than_monthly@np.where(_monthly_allowed, _total_hourly_parking_cost < _monthly_parking_cost,True)",1
+Hourly parking is best,_hourly_best@(_hourly_stalls_available & _hourly_cheaper_than_daily & _hourly_cheaper_than_monthly),1
+Daily is cheaper than hourly,"_daily_cheaper_than_hourly@np.where(_hourly_stalls_available, _daily_parking_cost < _total_hourly_parking_cost,True)",1
+Daily is cheaper than monthly,"_daily_cheaper_than_monthly@np.where(_monthly_allowed, _daily_parking_cost < _monthly_parking_cost,True)",1
+Daily parking is best,_daily_best@(_daily_stalls_available & _daily_cheaper_than_hourly & _daily_cheaper_than_monthly),1
+Monthly is cheaper than hourly,"_monthly_cheaper_than_hourly@np.where(_hourly_stalls_available, _monthly_parking_cost < _total_hourly_parking_cost,True)",1
+Monthly is cheaper than daily,"_monthly_cheaper_than_daily@np.where(_daily_stalls_available, _monthly_parking_cost < _daily_stalls_available,True)",1
+Monthly parking is best,_monthly_best@(_monthly_allowed & _monthly_cheaper_than_hourly & _monthly_cheaper_than_daily),1
+Parking size,_parking_size@(_hourly_best * _hourly_stalls) + (_daily_best * _daily_stalls) + (_monthly_best * _monthly_stalls),1
+Parking cost,_parking_cost@(_hourly_best * _total_hourly_parking_cost) + (_daily_best * _daily_parking_cost) + (_monthly_best * _monthly_parking_cost),1
+Parking to destination distance skim,_walk_distance@pd_skims['DISTWALK'],1
+# actual utility terms starts from here
+Log of parking size,"@np.where(_parking_size>0,np.log(_parking_size),-9999)",1
+Cost work trip,"@np.where(df.purpose == 'work', _parking_cost, 0)",coef_cost_work_trip
+Cost other trip,"@np.where(df.purpose != 'work', _parking_cost, 0)",coef_cost_other_trip
+Walk distance,@_walk_distance,coef_walk_distance
+Walk Unavailability,"@np.where(_walk_distance == 0, 1, 0)",coef_unavailable
+Require parking zone to be less than 0.75 mile from dest,"@np.where(_walk_distance > 0.75, 1, 0) * (df.destination != df.parking_zone)",coef_unavailable
\ No newline at end of file
diff --git a/test/parking_location/configs/parking_location_choice.yaml b/test/parking_location/configs/parking_location_choice.yaml
new file mode 100644
index 000000000..e7992c7e6
--- /dev/null
+++ b/test/parking_location/configs/parking_location_choice.yaml
@@ -0,0 +1,22 @@
+SPECIFICATION: parking_location_choice.csv
+
+COEFFICIENTS: parking_location_choice_coeffs.csv
+
+PREPROCESSOR:
+  SPEC: parking_location_choice_annotate_trips_preprocessor
+  DF: trips_merged
+  TABLES:
+    - land_use
+    - persons
+
+# boolean column to filter choosers (True means keep)
+CHOOSER_FILTER_COLUMN_NAME: is_park_eligible
+CHOOSER_SEGMENT_COLUMN_NAME: parking_segment
+
+ALTERNATIVE_FILTER_COLUMN_NAME: is_parking_zone
+ALT_DEST_COL_NAME: parking_zone
+
+TRIP_DEPARTURE_PERIOD: 'stop_period'
+
+TRIP_ORIGIN: origin
+TRIP_DESTINATION: destination
diff --git a/test/parking_location/configs/parking_location_choice_annotate_trips_preprocessor.csv b/test/parking_location/configs/parking_location_choice_annotate_trips_preprocessor.csv
new file mode 100644
index 000000000..a49f48b12
--- /dev/null
+++ b/test/parking_location/configs/parking_location_choice_annotate_trips_preprocessor.csv
@@ -0,0 +1,6 @@
+Description,Target,Expression
+Person Type,ptype,"reindex(persons.ptype, df.person_id)"
+Trip mode is drive,drive_trip,df.trip_mode <= 8
+Parking segment,parking_segment,setting('segment')
+Parking eligible trip,is_park_eligible,(drive_trip & (df.purpose != "home"))
+Activity duration,activity_duration,df.activity_duration_in_hours
diff --git a/test/parking_location/configs/parking_location_choice_coeffs.csv b/test/parking_location/configs/parking_location_choice_coeffs.csv
new file mode 100644
index 000000000..0edccfbf1
--- /dev/null
+++ b/test/parking_location/configs/parking_location_choice_coeffs.csv
@@ -0,0 +1,5 @@
+coefficient_name,value,constrain
+coef_unavailable,-9999,T
+coef_walk_distance,-11.8,F
+coef_cost_work_trip,-0.0072,F
+coef_cost_other_trip,-0.0041,F
diff --git a/test/parking_location/configs/settings.yaml b/test/parking_location/configs/settings.yaml
new file mode 100644
index 000000000..1cd739cb4
--- /dev/null
+++ b/test/parking_location/configs/settings.yaml
@@ -0,0 +1,62 @@
+# input tables
+input_table_list:
+  - tablename: households
+    filename: households.csv
+    index_col: household_id
+    rename_columns:
+      unique_hh_id: household_id
+      NP: hhsize
+      hh_workers_from_esr: num_workers
+      VEH: auto_ownership
+      MAZ: home_zone_id
+      HINCP: income
+    keep_columns:
+      - home_zone_id
+      - income
+      - hhsize
+      - HHT
+      - auto_ownership
+      - num_workers
+  - tablename: persons
+    filename: persons.csv
+    index_col: person_id
+    rename_columns:
+      unique_hh_id: household_id
+      AGEP: age
+      SPORDER: PNUM
+      SEX: sex
+      employed: pemploy
+      student_status: pstudent
+      person_type: ptype
+    keep_columns:
+      - household_id
+      - age
+      - PNUM
+      - sex
+      - pemploy
+      - pstudent
+      - ptype
+  - tablename: land_use
+    filename: land_use.csv
+    index_col: zone_id
+    rename_columns:
+      MAZ_ORIGINAL: zone_id
+      CountyID: county_id
+      TAZ_ORIGINAL: TAZ
+      DistID: DISTRICT
+      HH: TOTHH
+      POP: TOTPOP
+      ACRES: TOTACRE
+      emp_total: TOTEMP
+    keep_columns:
+      - TAZ
+      - DISTRICT
+      - SD
+      - county_id
+      - TOTHH
+      - TOTPOP
+      - TOTACRE
+
+trace_hh_id: 5924
+
+segment: "no_segmentation"
\ No newline at end of file
diff --git a/test/parking_location/configs/tour_departure_and_duration_alternatives.csv b/test/parking_location/configs/tour_departure_and_duration_alternatives.csv
new file mode 100644
index 000000000..bddab06b9
--- /dev/null
+++ b/test/parking_location/configs/tour_departure_and_duration_alternatives.csv
@@ -0,0 +1,191 @@
+start,end
+5,5
+5,6
+5,7
+5,8
+5,9
+5,10
+5,11
+5,12
+5,13
+5,14
+5,15
+5,16
+5,17
+5,18
+5,19
+5,20
+5,21
+5,22
+5,23
+6,6
+6,7
+6,8
+6,9
+6,10
+6,11
+6,12
+6,13
+6,14
+6,15
+6,16
+6,17
+6,18
+6,19
+6,20
+6,21
+6,22
+6,23
+7,7
+7,8
+7,9
+7,10
+7,11
+7,12
+7,13
+7,14
+7,15
+7,16
+7,17
+7,18
+7,19
+7,20
+7,21
+7,22
+7,23
+8,8
+8,9
+8,10
+8,11
+8,12
+8,13
+8,14
+8,15
+8,16
+8,17
+8,18
+8,19
+8,20
+8,21
+8,22
+8,23
+9,9
+9,10
+9,11
+9,12
+9,13
+9,14
+9,15
+9,16
+9,17
+9,18
+9,19
+9,20
+9,21
+9,22
+9,23
+10,10
+10,11
+10,12
+10,13
+10,14
+10,15
+10,16
+10,17
+10,18
+10,19
+10,20
+10,21
+10,22
+10,23
+11,11
+11,12
+11,13
+11,14
+11,15
+11,16
+11,17
+11,18
+11,19
+11,20
+11,21
+11,22
+11,23
+12,12
+12,13
+12,14
+12,15
+12,16
+12,17
+12,18
+12,19
+12,20
+12,21
+12,22
+12,23
+13,13
+13,14
+13,15
+13,16
+13,17
+13,18
+13,19
+13,20
+13,21
+13,22
+13,23
+14,14
+14,15
+14,16
+14,17
+14,18
+14,19
+14,20
+14,21
+14,22
+14,23
+15,15
+15,16
+15,17
+15,18
+15,19
+15,20
+15,21
+15,22
+15,23
+16,16
+16,17
+16,18
+16,19
+16,20
+16,21
+16,22
+16,23
+17,17
+17,18
+17,19
+17,20
+17,21
+17,22
+17,23
+18,18
+18,19
+18,20
+18,21
+18,22
+18,23
+19,19
+19,20
+19,21
+19,22
+19,23
+20,20
+20,21
+20,22
+20,23
+21,21
+21,22
+21,23
+22,22
+22,23
+23,23
\ No newline at end of file
diff --git a/test/parking_location/notebook/parking-location-results-summary.twbx b/test/parking_location/notebook/parking-location-results-summary.twbx
new file mode 100644
index 000000000..fa837b590
Binary files /dev/null and b/test/parking_location/notebook/parking-location-results-summary.twbx differ
diff --git a/test/parking_location/test_parking_location.py b/test/parking_location/test_parking_location.py
new file mode 100644
index 000000000..6859c93c4
--- /dev/null
+++ b/test/parking_location/test_parking_location.py
@@ -0,0 +1,458 @@
+import logging
+import pytest
+import os
+import shutil
+import pandas as pd
+import numpy as np
+from numpy import dot
+from numpy.linalg import norm
+import orca
+
+# import models is necessary to initalize the model steps with orca
+from activitysim.abm import models
+from activitysim.core import pipeline, config
+from activitysim.core import tracing
+
+from activitysim.core import simulate
+from activitysim.core import config
+from activitysim.abm.models.util import estimation
+import yaml
+
+logger = logging.getLogger(__name__)
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def module() -> str:
+    """
+    A pytest fixture that returns the data folder location.
+    :return: folder location for any necessary data to initialize the tests
+    """
+    return "parking_location"
+
+
+# Used by conftest.py initialize_pipeline method
+@pytest.fixture(scope="module")
+def tables(prepare_module_inputs) -> dict[str, str]:
+    """
+    A pytest fixture that returns the "mock" tables to build pipeline dataframes. The
+    key-value pair is the name of the table and the index column.
+    :return: dict
+    """
+    return {
+        "land_use": "maz",
+        "persons": "person_id",
+        "households": "household_id",
+        "accessibility": "maz",
+        "tours": "tour_id",
+        "trips": "trip_id",
+    }
+
+
+# Used by conftest.py initialize_pipeline method
+# Set to true if you need to read skims into the pipeline
+@pytest.fixture(scope="module")
+def initialize_network_los() -> bool:
+    """
+    A pytest boolean fixture indicating whether network skims should be read from the
+    fixtures test data folder.
+    :return: boolcls
+    """
+    return True
+
+
+@pytest.fixture(scope="module")
+def load_checkpoint() -> bool:
+    """
+    checkpoint to be loaded from the pipeline when reconnecting.
+    """
+    return "initialize_households"
+
+
+# make a reconnect_pipeline internal to test module
+@pytest.mark.skipif(
+    os.path.isfile("test/parking_location/output/pipeline.h5"),
+    reason="no need to recreate pipeline store if already exist",
+)
+def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog):
+    # Run summarize model
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(models=["initialize_landuse", "initialize_households"])
+
+    # save the updated pipeline tables
+    person_df = pipeline.get_table("persons")
+    person_df.to_csv("test/parking_location/output/person.csv")
+
+    household_df = pipeline.get_table("households")
+    household_df.to_csv("test/parking_location/output/household.csv")
+
+    land_use_df = pipeline.get_table("land_use")
+    land_use_df.to_csv("test/parking_location/output/land_use.csv")
+
+    accessibility_df = pipeline.get_table("accessibility")
+    accessibility_df.to_csv("test/parking_location/output/accessibility.csv")
+
+    tours_df = pipeline.get_table("tours")
+    tours_df.to_csv("test/parking_location/output/tours.csv")
+
+    trips_df = pipeline.get_table("trips")
+    trips_df.to_csv("test/parking_location/output/trips.csv")
+
+    pipeline.close_pipeline()
+
+
+# @pytest.mark.skip
+def test_parking_location(reconnect_pipeline: pipeline.Pipeline, caplog):
+    # Run summarize model
+    caplog.set_level(logging.INFO)
+
+    # run model step
+    pipeline.run(models=["parking_location"], resume_after="initialize_households")
+
+    # get the updated trips data
+    trips_df = pipeline.get_table("trips")
+    trips_df.to_csv("test/parking_location/output/trips_after_parking_choice.csv")
+
+
+# fetch/prepare existing files for model inputs
+# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim
+@pytest.fixture(scope="module")
+def prepare_module_inputs() -> None:
+    """
+
+    copy input files from sharepoint into test folder
+
+    create unique person id in person file
+
+    :return: None
+    """
+    # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/
+    # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1
+    test_dir = os.path.join("test", "parking_location", "data")
+
+    accessibility_file = os.path.join(
+        "test", "parking_location", "data", "accessibilities.csv"
+    )
+    household_file = os.path.join(test_dir, "popsyn", "households.csv")
+    person_file = os.path.join(test_dir, "popsyn", "persons.csv")
+    landuse_file = os.path.join(test_dir, "landuse", "maz_data_withDensity.csv")
+
+    shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv"))
+    shutil.copy(household_file, os.path.join(test_dir, "households.csv"))
+    shutil.copy(person_file, os.path.join(test_dir, "persons.csv"))
+    shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv"))
+
+    # add original maz id to accessibility table
+    land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv"))
+
+    land_use_df.rename(
+        columns={"MAZ": "maz", "MAZ_ORIGINAL": "maz_county_based"}, inplace=True
+    )
+
+    land_use_df.to_csv(os.path.join(test_dir, "land_use.csv"), index=False)
+
+    accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv"))
+
+    accessibility_df["maz"] = accessibility_df["mgra"]
+
+    accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False)
+
+    # currently household file has to have these two columns, even before annotation
+    # because annotate person happens before household and uses these two columns
+    # TODO find a way to get around this
+    ####
+    household_df = pd.read_csv(os.path.join(test_dir, "households.csv"))
+
+    household_columns_dict = {
+        "HHID": "household_id",
+        "TAZ": "taz",
+        "MAZ": "maz_county_based",
+    }
+
+    household_df.rename(columns=household_columns_dict, inplace=True)
+
+    tm2_simulated_household_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "householdData_1.csv")
+    )
+    tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    household_df = pd.merge(
+        household_df,
+        tm2_simulated_household_df[
+            [
+                "household_id",
+                "autos",
+                "automated_vehicles",
+                "transponder",
+                "cdap_pattern",
+                "jtf_choice",
+            ]
+        ],
+        how="inner",  # tm2 is not 100% sample run
+        on="household_id",
+    )
+
+    household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False)
+
+    person_df = pd.read_csv(os.path.join(test_dir, "persons.csv"))
+
+    person_columns_dict = {"HHID": "household_id", "PERID": "person_id"}
+
+    person_df.rename(columns=person_columns_dict, inplace=True)
+
+    tm2_simulated_person_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "personData_1.csv")
+    )
+    tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    person_df = pd.merge(
+        person_df,
+        tm2_simulated_person_df[
+            [
+                "household_id",
+                "person_id",
+                "person_num",
+                "type",
+                "value_of_time",
+                "activity_pattern",
+                "imf_choice",
+                "inmf_choice",
+                "fp_choice",
+                "reimb_pct",
+                "workDCLogsum",
+                "schoolDCLogsum",
+            ]
+        ],
+        how="inner",  # tm2 is not 100% sample run
+        on=["household_id", "person_id"],
+    )
+
+    # get tm2 simulated workplace and school location results
+    tm2_simulated_wsloc_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "wsLocResults_1.csv")
+    )
+    tm2_simulated_wsloc_df.rename(
+        columns={"HHID": "household_id", "PersonID": "person_id"}, inplace=True
+    )
+
+    person_df = pd.merge(
+        person_df,
+        tm2_simulated_wsloc_df[
+            [
+                "household_id",
+                "person_id",
+                "WorkLocation",
+                "WorkLocationLogsum",  # this is the same as `workDCLogsum` in tm2 person output
+                "SchoolLocation",
+                "SchoolLocationLogsum",  # this is the same as `schoolDCLogsum` in tm2 person output
+            ]
+        ],
+        how="inner",  # ctramp might not be 100% sample run
+        on=["household_id", "person_id"],
+    )
+
+    person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False)
+
+    ## get tour data from tm2 output
+
+    tm2_simulated_indiv_tour_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "indivTourData_1.csv")
+    )
+    tm2_simulated_joint_tour_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "jointTourData_1.csv")
+    )
+
+    tm2_simulated_tour_df = pd.concat(
+        [tm2_simulated_indiv_tour_df, tm2_simulated_joint_tour_df],
+        sort=False,
+        ignore_index=True,
+    )
+
+    tm2_simulated_tour_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    tm2_simulated_tour_df["unique_tour_id"] = range(1, len(tm2_simulated_tour_df) + 1)
+
+    ## get trip data from tm2 output
+    tm2_simulated_indiv_trip_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "indivTripData_1.csv")
+    )
+    tm2_simulated_joint_trip_df = pd.read_csv(
+        os.path.join(test_dir, "tm2_outputs", "jointTripData_1.csv")
+    )
+
+    tm2_simulated_trip_df = pd.concat(
+        [tm2_simulated_indiv_trip_df, tm2_simulated_joint_trip_df],
+        sort=False,
+        ignore_index=True,
+    )
+
+    tm2_simulated_trip_df.rename(columns={"hh_id": "household_id"}, inplace=True)
+
+    tm2_simulated_trip_df["trip_id"] = range(1, len(tm2_simulated_trip_df) + 1)
+
+    tm2_simulated_trip_df = pd.merge(
+        tm2_simulated_trip_df,
+        tm2_simulated_tour_df[
+            [
+                "household_id",
+                "person_id",
+                "tour_id",
+                "tour_purpose",
+                "unique_tour_id",
+                "start_period",
+                "end_period",
+            ]
+        ],
+        how="left",
+        on=["household_id", "person_id", "tour_id", "tour_purpose"],
+    )
+    # drop tour id and rename unique_tour_id to tour_id
+    tm2_simulated_tour_df.drop(["tour_id"], axis=1, inplace=True)
+    tm2_simulated_tour_df.rename(columns={"unique_tour_id": "tour_id"}, inplace=True)
+
+    tm2_simulated_trip_df.drop(["tour_id"], axis=1, inplace=True)
+    tm2_simulated_trip_df.rename(
+        columns={
+            "unique_tour_id": "tour_id",
+            "orig_mgra": "origin",
+            "dest_mgra": "destination",
+            "start_period": "tour_start_period",
+            "end_period": "tour_end_period",
+        },
+        inplace=True,
+    )
+
+    tm2_simulated_trip_df["purpose"] = tm2_simulated_trip_df["dest_purpose"].str.lower()
+
+    period_map_df = pd.read_csv(os.path.join(test_dir, "period_mapping_mtc.csv"))
+
+    tm2_simulated_trip_df.sort_values(
+        by=["household_id", "person_id", "person_num", "stop_period", "tour_id"],
+        inplace=True,
+    )
+
+    tm2_simulated_trip_df = pd.merge(
+        tm2_simulated_trip_df,
+        period_map_df,
+        left_on="stop_period",
+        right_on="period",
+        how="left",
+    )
+
+    tm2_simulated_trip_df["trip_departure_time"] = tm2_simulated_trip_df[
+        "minutes_reference"
+    ]
+    tm2_simulated_trip_df["trip_arrival_time"] = (
+        tm2_simulated_trip_df["trip_departure_time"]
+        + tm2_simulated_trip_df["TRIP_TIME"]
+    )
+    tm2_simulated_trip_df["next_trip_departure_time"] = tm2_simulated_trip_df.groupby(
+        ["household_id", "person_id", "person_num"]
+    )["trip_arrival_time"].shift(-1)
+
+    # activity duration is start of next trip - end of current trip
+    tm2_simulated_trip_df["activity_duration"] = (
+        tm2_simulated_trip_df["next_trip_departure_time"]
+        - tm2_simulated_trip_df["trip_arrival_time"]
+    )
+
+    # set default activity duration to 5 minutes
+    tm2_simulated_trip_df["activity_duration"] = np.where(
+        tm2_simulated_trip_df["activity_duration"] > 0,
+        tm2_simulated_trip_df["activity_duration"],
+        5,
+    )
+
+    # activity duration in hours
+    tm2_simulated_trip_df["activity_duration_in_hours"] = (
+        tm2_simulated_trip_df["activity_duration"] / 60
+    )
+
+    tm2_simulated_trip_df.drop(
+        [
+            "period",
+            "start_time",
+            "end_time",
+            "minutes_reference",
+            "trip_departure_time",
+            "trip_arrival_time",
+            "next_trip_departure_time",
+            "activity_duration",
+        ],
+        inplace=True,
+        axis=1,
+    )
+
+    # randomly select 100,000 trips to run the parking location choice model
+    # memory constraint on `logit.interaction_dataset` for large trips table
+    # tm2_simulated_trip_df = tm2_simulated_trip_df.sample(100000, random_state=1)
+
+    # select only if target parking maz is > 0
+    tm2_simulated_trip_df = tm2_simulated_trip_df[
+        tm2_simulated_trip_df["parking_mgra"] > 0
+    ]
+
+    tm2_simulated_tour_df = tm2_simulated_tour_df[
+        tm2_simulated_tour_df.household_id.isin(tm2_simulated_trip_df.household_id)
+    ]
+    tm2_simulated_tour_df.to_csv(os.path.join(test_dir, "tours.csv"), index=False)
+
+    tm2_simulated_trip_df.to_csv(os.path.join(test_dir, "trips.csv"), index=False)
+
+
+def create_summary(input_df, key, out_col="Share") -> pd.DataFrame:
+    """
+    Create summary for the input data.
+    1. group input data by the "key" column
+    2. calculate the percent of input data records in each "key" category.
+
+    :return: pd.DataFrame
+    """
+
+    out_df = input_df.groupby(key).size().reset_index(name="Count")
+    out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4)
+
+    return out_df[[key, out_col]]
+
+
+def cosine_similarity(a, b):
+    """
+    Computes cosine similarity between two vectors.
+
+    Cosine similarity is used here as a metric to measure similarity between two sequence of numbers.
+    Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them
+    i.e., dot products of the vectors divided by the product of their lengths.
+
+    :return:
+    """
+
+    return dot(a, b) / (norm(a) * norm(b))
+
+
+def compare_simulated_against_target(
+    target_df: pd.DataFrame,
+    simulated_df: pd.DataFrame,
+    target_key: str,
+    simulated_key: str,
+) -> bool:
+    """
+    compares the simulated and target results by computing the cosine similarity between them.
+
+    :return:
+    """
+
+    merged_df = pd.merge(
+        target_df, simulated_df, left_on=target_key, right_on=simulated_key, how="outer"
+    )
+    merged_df = merged_df.fillna(0)
+
+    logger.info("simulated vs target share:\n%s" % merged_df)
+
+    similarity_value = cosine_similarity(
+        merged_df["Target_Share"].tolist(), merged_df["Simulated_Share"].tolist()
+    )
+
+    logger.info("cosine similarity:\n%s" % similarity_value)
+
+    return similarity_value