diff --git a/_shared_utils/shared_utils/rt_dates.py b/_shared_utils/shared_utils/rt_dates.py index fb2e42886..a76c38953 100644 --- a/_shared_utils/shared_utils/rt_dates.py +++ b/_shared_utils/shared_utils/rt_dates.py @@ -73,6 +73,7 @@ "oct2024f": "2024-10-20", "oct2024g": "2024-10-21", # additional one-off to capture Amtrak in HQTA "nov2024": "2024-11-13", + "dec2024": "2024-12-11" } y2023_dates = [ diff --git a/ca_transit_speed_maps/update_vars_index.py b/ca_transit_speed_maps/update_vars_index.py index 6a94b5184..3af4b4d49 100644 --- a/ca_transit_speed_maps/update_vars_index.py +++ b/ca_transit_speed_maps/update_vars_index.py @@ -18,7 +18,7 @@ catalog = catalog_utils.get_catalog('gtfs_analytics_data') -ANALYSIS_DATE = dt.date.fromisoformat(rt_dates.DATES['nov2024']) +ANALYSIS_DATE = dt.date.fromisoformat(rt_dates.DATES['dec2024']) PROGRESS_PATH = f'./_rt_progress_{ANALYSIS_DATE}.parquet' GEOJSON_SUBFOLDER = f'segment_speeds_{ANALYSIS_DATE}/' diff --git a/gtfs_funnel/Makefile b/gtfs_funnel/Makefile index e978363a0..a98a88e67 100644 --- a/gtfs_funnel/Makefile +++ b/gtfs_funnel/Makefile @@ -51,7 +51,7 @@ all: # update open_data/update_vars.py cd ../open_data/ && make create_gtfs_schedule_geospatial_open_data -f Makefile # update high_quality_transit_areas/update_vars.py - # cd ../high_quality_transit_areas/ && make hqta_data -f Makefile + cd ../high_quality_transit_areas/ && make hqta_data -f Makefile # update rt_segment_speeds/segment_speed_utils/project_vars.py cd ../rt_segment_speeds/scripts/ && make all_speeds_pipeline -f Makefile && cd ../../ # update rt_scheduled_v_ran/scripts/update_vars.py diff --git a/gtfs_funnel/crosswalk_gtfs_dataset_key_to_organization.py b/gtfs_funnel/crosswalk_gtfs_dataset_key_to_organization.py index 709221f68..9b3ed4cb3 100644 --- a/gtfs_funnel/crosswalk_gtfs_dataset_key_to_organization.py +++ b/gtfs_funnel/crosswalk_gtfs_dataset_key_to_organization.py @@ -59,11 +59,11 @@ def load_ntd(year: int) -> pd.DataFrame: Select certain columns. """ df = ( - tbls.mart_ntd.dim_annual_ntd_agency_information() + tbls.mart_ntd.dim_annual_agency_information() >> filter(_.year == year, _.state == "CA", _._is_current == True) >> select( _.number_of_state_counties, - _.uza_name, + _.primary_uza_name, _.density, _.number_of_counties_with_service, _.state_admin_funds_expended, @@ -71,7 +71,7 @@ def load_ntd(year: int) -> pd.DataFrame: _.population, _.service_area_pop, _.subrecipient_type, - _.primary_uza, + _.primary_uza_code, _.reporter_type, _.organization_type, _.agency_name, diff --git a/gtfs_funnel/logs/download_data.log b/gtfs_funnel/logs/download_data.log index cecb4ab5e..833f5b529 100644 --- a/gtfs_funnel/logs/download_data.log +++ b/gtfs_funnel/logs/download_data.log @@ -678,3 +678,19 @@ 2024-11-15 10:19:16.119 | INFO | __main__:download_one_day:29 - # operators to run: 194 2024-11-15 10:19:16.120 | INFO | __main__:download_one_day:33 - *********** Download st data *********** 2024-11-15 10:21:15.285 | INFO | __main__:download_one_day:56 - execution time: 0:02:00.819066 +2024-12-17 12:59:10.547 | INFO | __main__:download_one_day:45 - Analysis date: 2024-12-11 +2024-12-17 12:59:14.245 | INFO | __main__:download_one_day:52 - # operators to run: 226 +2024-12-17 12:59:14.249 | INFO | __main__:download_one_day:56 - *********** Download trips data *********** +2024-12-17 12:59:49.103 | INFO | __main__:download_one_day:86 - execution time: 0:00:38.555174 +2024-12-17 13:00:06.712 | INFO | __main__:download_one_day:22 - Analysis date: 2024-12-11 +2024-12-17 13:00:09.087 | INFO | __main__:download_one_day:29 - # operators to run: 226 +2024-12-17 13:00:09.087 | INFO | __main__:download_one_day:33 - *********** Download stops data *********** +2024-12-17 13:00:20.110 | INFO | __main__:download_one_day:64 - execution time: 0:00:13.397006 +2024-12-17 13:00:36.224 | INFO | __main__:download_one_day:22 - Analysis date: 2024-12-11 +2024-12-17 13:00:38.381 | INFO | __main__:download_one_day:29 - # operators to run: 226 +2024-12-17 13:00:38.382 | INFO | __main__:download_one_day:33 - *********** Download routelines data *********** +2024-12-17 13:02:42.410 | INFO | __main__:download_one_day:63 - execution time: 0:02:06.185085 +2024-12-17 13:02:59.291 | INFO | __main__:download_one_day:21 - Analysis date: 2024-12-11 +2024-12-17 13:03:01.386 | INFO | __main__:download_one_day:29 - # operators to run: 194 +2024-12-17 13:03:01.387 | INFO | __main__:download_one_day:33 - *********** Download st data *********** +2024-12-17 13:04:59.321 | INFO | __main__:download_one_day:56 - execution time: 0:02:00.030094 diff --git a/gtfs_funnel/logs/download_vp_v2.log b/gtfs_funnel/logs/download_vp_v2.log index dfb44b193..52763e3dc 100644 --- a/gtfs_funnel/logs/download_vp_v2.log +++ b/gtfs_funnel/logs/download_vp_v2.log @@ -438,3 +438,14 @@ 2024-11-15 10:35:51.779 | INFO | __main__::112 - export concatenated vp: 0:03:55.144496 2024-11-15 10:39:51.367 | INFO | __main__::134 - remove batched parquets 2024-11-15 10:39:51.367 | INFO | __main__::137 - execution time: 0:08:04.043473 +2024-12-17 13:05:19.418 | INFO | __main__::148 - Analysis date: 2024-12-11 +2024-12-17 13:07:43.522 | INFO | __main__:loop_through_batches_and_download_vp:111 - exported batch 0 to GCS: 0:02:24.103479 +2024-12-17 13:08:53.304 | INFO | __main__:loop_through_batches_and_download_vp:111 - exported batch 1 to GCS: 0:01:09.780570 +2024-12-17 13:12:58.734 | INFO | __main__:loop_through_batches_and_download_vp:111 - exported batch 2 to GCS: 0:04:05.429329 +2024-12-17 13:14:54.294 | INFO | __main__:loop_through_batches_and_download_vp:111 - exported batch 3 to GCS: 0:01:55.559501 +2024-12-17 13:14:54.295 | INFO | __main__::155 - execution time: 0:09:34.875675 +2024-12-17 13:15:11.818 | INFO | __main__::97 - Analysis date: 2024-12-11 +2024-12-17 13:15:19.487 | INFO | __main__::105 - concat and filter batched data: 0:00:07.667599 +2024-12-17 13:19:32.102 | INFO | __main__::112 - export concatenated vp: 0:04:12.615694 +2024-12-17 13:23:34.976 | INFO | __main__::134 - remove batched parquets +2024-12-17 13:23:34.976 | INFO | __main__::137 - execution time: 0:08:23.157313 diff --git a/gtfs_funnel/logs/vp_preprocessing.log b/gtfs_funnel/logs/vp_preprocessing.log index e21eb281b..fed013952 100644 --- a/gtfs_funnel/logs/vp_preprocessing.log +++ b/gtfs_funnel/logs/vp_preprocessing.log @@ -299,3 +299,13 @@ 2024-11-15 11:07:16.370 | INFO | __main__::236 - vp with dwell time 2024-11-13: 0:06:07.784141 2024-11-15 11:13:16.502 | INFO | __main__::121 - 2024-11-13: condense vp for trip 0:05:43.170466 2024-11-15 11:25:18.878 | INFO | __main__::129 - 2024-11-13: prepare vp to use in nearest neighbor: 0:12:02.376671 +2024-12-17 13:50:53.303 | INFO | __main__::169 - 2024-12-11: pare down vp: 0:02:22.877788 +2024-12-17 13:55:36.457 | INFO | __main__:attach_prior_vp_add_direction:90 - persist vp gddf: 0:04:24.416660 +2024-12-17 13:59:57.106 | INFO | __main__:attach_prior_vp_add_direction:122 - np vectorize arrays for direction: 0:04:20.649389 +2024-12-17 14:00:04.653 | INFO | __main__::194 - 2024-12-11: export vp direction: 0:08:52.613283 +2024-12-17 14:01:30.377 | INFO | __main__::200 - 2024-12-11: export usable vp with direction: 0:01:25.723882 +2024-12-17 14:01:30.378 | INFO | __main__::203 - 2024-12-11: vp_direction script execution time: 0:10:18.337165 +2024-12-17 14:06:53.796 | INFO | __main__::213 - compute dwell df: 0:04:47.332255 +2024-12-17 14:08:13.713 | INFO | __main__::235 - merge with original and export: 0:01:19.916578 +2024-12-17 14:08:13.714 | INFO | __main__::236 - vp with dwell time 2024-12-11: 0:06:07.248833 +2024-12-17 14:13:50.962 | INFO | __main__::78 - 2024-12-11: condense vp for trip 0:05:19.406518 diff --git a/gtfs_funnel/update_vars.py b/gtfs_funnel/update_vars.py index 3553d3441..b7b2fb422 100644 --- a/gtfs_funnel/update_vars.py +++ b/gtfs_funnel/update_vars.py @@ -12,7 +12,7 @@ ) -analysis_date_list = [rt_dates.DATES["nov2024"]] +analysis_date_list = [rt_dates.DATES["dec2024"]] # analysis_date_list = all_dates GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data") diff --git a/high_quality_transit_areas/09_enforce_collinearity.ipynb b/high_quality_transit_areas/09_enforce_collinearity.ipynb index ffe555def..ee8352158 100644 --- a/high_quality_transit_areas/09_enforce_collinearity.ipynb +++ b/high_quality_transit_areas/09_enforce_collinearity.ipynb @@ -267,6 +267,37 @@ "s = pd.Series(share_counts.values())" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a987c46-8fcc-40e0-9b47-0bdd3a444666", + "metadata": {}, + "outputs": [], + "source": [ + "import altair as alt\n", + "# from vega_datasets import data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89753eb6-68e0-402a-bb8e-134649923ef7", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame({'Shared Stop Count':s})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40052a5d-ef99-41a7-88e3-f2524f6c1d21", + "metadata": {}, + "outputs": [], + "source": [ + "df = df[df['Shared Stop Count'] <= 10]" + ] + }, { "cell_type": "code", "execution_count": null, @@ -423,7 +454,7 @@ }, "outputs": [], "source": [ - "feed_names_filtered = feed_names >> filter(_.name.str.contains('Sac'))\n", + "feed_names_filtered = feed_names >> filter(_.name.str.contains('VTA'))\n", "display(feed_names_filtered)\n", "gtfs_dataset_key = feed_names_filtered.gtfs_dataset_key.iloc[0]" ] @@ -875,7 +906,7 @@ "metadata": {}, "outputs": [], "source": [ - "combined_export.to_parquet(f\"{GCS_FILE_PATH}max_arrivals_by_stop.parquet\")" + "# combined_export.to_parquet(f\"{GCS_FILE_PATH}max_arrivals_by_stop.parquet\")" ] }, { @@ -903,7 +934,7 @@ "metadata": {}, "outputs": [], "source": [ - "ms = areas >> filter(_.hqta_type == ('major_stop_bus'))" + "ms = areas >> filter(_.hqta_type.str.contains('major_stop'))" ] }, { @@ -993,36 +1024,48 @@ "metadata": {}, "outputs": [], "source": [ - "one_pts = hqta_points >> filter(_.agency_primary.str.contains('Sacramento'))" + "# one_pts = hqta_points >> filter(_.agency_primary.str.contains('Los Angeles County'))\n", + "one_pts = hqta_points >> filter(_.hqta_details != 'corridor_other_stop')" ] }, { "cell_type": "code", "execution_count": null, - "id": "d643893d-f0a0-4f7c-b617-f193d9e62dd6", + "id": "5c243176-56eb-4ffb-9623-a6b8a009c18b", "metadata": {}, "outputs": [], "source": [ - "one_pts.explore(column='hqta_type')" + "from calitp_data_analysis.geography_utils import CA_NAD83Albers" ] }, { "cell_type": "code", "execution_count": null, - "id": "0131bb42-d63b-4142-915f-e4ce29972d35", + "id": "b9252b23-3e3d-4b87-958d-98af1171bdc4", "metadata": {}, "outputs": [], "source": [ - "areas.to_file('hqta_areas_update.geojson')\n", - "hqta_points.to_file('hqta_points_update.geojson')" + "one_pts = one_pts.to_crs(CA_NAD83Albers)" ] }, { - "cell_type": "markdown", - "id": "4b351da3-4825-4caa-8333-33386db868f1", + "cell_type": "code", + "execution_count": null, + "id": "fead1157-a0d5-4947-be13-580d6dc6edea", + "metadata": {}, + "outputs": [], + "source": [ + "one_pts.geometry = one_pts.buffer(100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d643893d-f0a0-4f7c-b617-f193d9e62dd6", "metadata": {}, + "outputs": [], "source": [ - "## all bus" + "one_pts.explore(column='hqta_type', tiles=\"Cartodb Positron\")" ] }, { @@ -1032,8 +1075,8 @@ "metadata": {}, "outputs": [], "source": [ - "# all_bus = gpd.read_parquet(f\"{GCS_FILE_PATH}all_bus.parquet\")\n", - "hqta_segments = gpd.read_parquet(f\"{GCS_FILE_PATH}hqta_segments.parquet\")" + "all_bus = gpd.read_parquet(f\"{GCS_FILE_PATH}all_bus.parquet\")\n", + "# hqta_segments = gpd.read_parquet(f\"{GCS_FILE_PATH}hqta_segments.parquet\")" ] }, { @@ -1053,7 +1096,17 @@ "metadata": {}, "outputs": [], "source": [ - "hqta_segments = find_inconclusive_directions(hqta_segments)" + "# hqta_segments = find_inconclusive_directions(hqta_segments)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53421714-f669-4413-8192-b658bc5af47c", + "metadata": {}, + "outputs": [], + "source": [ + "# hqta_segments" ] }, { @@ -1063,7 +1116,7 @@ "metadata": {}, "outputs": [], "source": [ - "# one = hqta_segments >> filter(_.schedule_gtfs_dataset_key == '70c8a8b71c815224299523bf2115924a')" + "one = all_bus >> filter(_.schedule_gtfs_dataset_key == 'fb467982dcc77a7f9199bebe709bb700')" ] }, { @@ -1073,7 +1126,7 @@ "metadata": {}, "outputs": [], "source": [ - "# one.geometry = one.buffer(35)" + "one.geometry = one.buffer(35)" ] }, { @@ -1086,6 +1139,16 @@ "# one = one >> filter(_.route_id.isin(['228', '227', '062', '061', '056', '106']))" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "56688d44-b377-4b74-8a32-d7f2d2dff3db", + "metadata": {}, + "outputs": [], + "source": [ + "one.explore(column='segment_direction')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/high_quality_transit_areas/Makefile b/high_quality_transit_areas/Makefile index d373f296b..40355558d 100644 --- a/high_quality_transit_areas/Makefile +++ b/high_quality_transit_areas/Makefile @@ -1,8 +1,8 @@ hqta_data: - python rail_ferry_brt_stops.py - python create_hqta_segments.py - python create_aggregate_stop_frequencies.py - python sjoin_stops_to_segments.py +# python rail_ferry_brt_stops.py +# python create_hqta_segments.py +# python create_aggregate_stop_frequencies.py +# python sjoin_stops_to_segments.py python prep_pairwise_intersections.py python get_intersections.py python create_bus_hqta_types.py diff --git a/high_quality_transit_areas/create_aggregate_stop_frequencies.py b/high_quality_transit_areas/create_aggregate_stop_frequencies.py index 66a9c9927..b13684f1d 100644 --- a/high_quality_transit_areas/create_aggregate_stop_frequencies.py +++ b/high_quality_transit_areas/create_aggregate_stop_frequencies.py @@ -184,7 +184,7 @@ def filter_qualifying_stops(one_stop_st: pd.DataFrame, qualify_pairs: list) -> p ) this_stop_route_dirs = (one_stop_st >> distinct(_.route_dir, _.route_dir_count)).route_dir.to_numpy() # preserves sort order aggregation_ok_route_dirs = check_stop(this_stop_route_dirs, qualify_pairs) - return one_stop_df >> filter(_.route_dir.isin(aggregation_ok_route_dirs)) + return one_stop_st >> filter(_.route_dir.isin(aggregation_ok_route_dirs)) def collinear_filter_feed( gtfs_dataset_key: str, diff --git a/high_quality_transit_areas/logs/hqta_processing.log b/high_quality_transit_areas/logs/hqta_processing.log index 7ecc07323..3b3f77a3a 100644 --- a/high_quality_transit_areas/logs/hqta_processing.log +++ b/high_quality_transit_areas/logs/hqta_processing.log @@ -117,3 +117,19 @@ 2024-12-13 16:11:06.186 | INFO | __main__::175 - C3_create_bus_hqta_types 2024-10-21 execution time: 0:00:16.642029 2024-12-13 16:11:42.067 | INFO | __main__::219 - D1_assemble_hqta_points 2024-10-21 execution time: 0:00:19.108129 2024-12-13 16:12:12.529 | INFO | __main__::168 - D2_assemble_hqta_polygons 2024-10-21 execution time: 0:00:12.842061 +2024-12-17 14:38:38.771 | INFO | __main__::277 - A1_rail_ferry_brt_stops 2024-12-11 execution time: 0:00:19.093442 +2024-12-17 14:47:04.101 | INFO | __main__::248 - B1_create_hqta_segments execution time: 0:08:09.120437 +2024-12-17 14:59:29.127 | INFO | __main__::277 - A1_rail_ferry_brt_stops 2024-12-11 execution time: 0:00:19.427916 +2024-12-17 15:05:20.491 | INFO | __main__::248 - B1_create_hqta_segments execution time: 0:05:35.265734 +2024-12-17 15:08:05.518 | INFO | __main__::333 - B2_create_aggregate_stop_frequencies 2024-12-11 execution time: 0:02:27.789099 +2024-12-17 15:08:34.978 | INFO | __main__::326 - B3_sjoin_stops_to_segments 2024-12-11 execution time: 0:00:12.351944 +2024-12-17 15:08:56.152 | INFO | __main__::147 - C1_prep_pairwise_intersections 2024-12-11 execution time: 0:00:04.328776 +2024-12-17 15:09:20.373 | INFO | __main__::124 - C2_find_intersections 2024-12-11 execution time: 0:00:07.923657 +2024-12-17 15:09:48.649 | INFO | __main__::175 - C3_create_bus_hqta_types 2024-12-11 execution time: 0:00:11.784740 +2024-12-17 15:10:23.397 | INFO | __main__::219 - D1_assemble_hqta_points 2024-12-11 execution time: 0:00:18.075380 +2024-12-17 15:10:53.886 | INFO | __main__::168 - D2_assemble_hqta_polygons 2024-12-11 execution time: 0:00:13.604352 +2024-12-19 14:06:19.225 | INFO | __main__::149 - C1_prep_pairwise_intersections 2024-12-11 execution time: 0:00:04.821081 +2024-12-19 14:06:46.848 | INFO | __main__::124 - C2_find_intersections 2024-12-11 execution time: 0:00:06.916373 +2024-12-19 14:07:17.276 | INFO | __main__::175 - C3_create_bus_hqta_types 2024-12-11 execution time: 0:00:13.000987 +2024-12-19 14:07:53.167 | INFO | __main__::219 - D1_assemble_hqta_points 2024-12-11 execution time: 0:00:18.462037 +2024-12-19 14:08:28.603 | INFO | __main__::168 - D2_assemble_hqta_polygons 2024-12-11 execution time: 0:00:17.899141 diff --git a/high_quality_transit_areas/prep_pairwise_intersections.py b/high_quality_transit_areas/prep_pairwise_intersections.py index b7db1c835..e7c33f304 100644 --- a/high_quality_transit_areas/prep_pairwise_intersections.py +++ b/high_quality_transit_areas/prep_pairwise_intersections.py @@ -49,7 +49,10 @@ def sjoin_against_other_operators( """ Spatial join of the in group vs the out group. This could be the operator vs other operators, - or a route vs other routes. + or a route vs other routes. This is currently + east-west vs north-south segments, which requires + the additional step of excluding intersections + resulting from the same route changing direction. Create a crosswalk / pairwise table showing these links. @@ -57,7 +60,7 @@ def sjoin_against_other_operators( computationally expensive, so we want to do it on fewer rows. """ - route_cols = ["hqta_segment_id", "segment_direction"] + route_cols = ["hqta_segment_id", "segment_direction", "route_key"] s1 = gpd.sjoin( in_group_df[route_cols + ["geometry"]], @@ -65,6 +68,8 @@ def sjoin_against_other_operators( how = "inner", predicate = "intersects" ).drop(columns = ["index_right", "geometry"]) + + s1 = s1[s1["route_key_left"] != s1["route_key_right"]] route_pairs = ( s1.rename( diff --git a/high_quality_transit_areas/update_vars.py b/high_quality_transit_areas/update_vars.py index 5cce03c52..2911e6499 100644 --- a/high_quality_transit_areas/update_vars.py +++ b/high_quality_transit_areas/update_vars.py @@ -1,7 +1,7 @@ from shared_utils import rt_dates import datetime as dt -analysis_date = rt_dates.DATES["oct2024g"] +analysis_date = rt_dates.DATES["dec2024"] GCS_FILE_PATH = ("gs://calitp-analytics-data/data-analyses/" "high_quality_transit_areas/") diff --git a/open_data/update_vars.py b/open_data/update_vars.py index 8c42de7f5..ba7aed422 100644 --- a/open_data/update_vars.py +++ b/open_data/update_vars.py @@ -1,7 +1,7 @@ from pathlib import Path from shared_utils import catalog_utils, rt_dates -analysis_date = rt_dates.DATES["nov2024"] +analysis_date = rt_dates.DATES["dec2024"] GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data") diff --git a/open_data/xml/ca_hq_transit_areas.xml b/open_data/xml/ca_hq_transit_areas.xml index 78d8a8dda..c088c54a4 100644 --- a/open_data/xml/ca_hq_transit_areas.xml +++ b/open_data/xml/ca_hq_transit_areas.xml @@ -1,307 +1,130 @@ - - - - - eng - - - - - utf8 - - - - - dataset - - - - dataset - - - - 2024-10-30 - - - ISO 19139 Geographic Information - Metadata - Implementation Specification - - - 2007 - - - - - - geometryOnly - - - - - - - composite - - - - - - - - - - - - 4326 - - - EPSG - - - 6.2(3.0.1) - - - - - - - - - - - ca_hq_transit_areas - - - - - 2022-02-08 - - - - creation - - - - - - - - 2024-10-16 - - - - revision - - - - - - - - Caltrans - - - - publisher - - - - - - - - Eric Dasmalchi - - - Caltrans - - - Data & Digital Services / California Integrated Travel Project - - - - - - - eric.dasmalchi@dot.ca.gov - - - - - - - - pointOfContact - - - - - - - mapDigital - - - - - - Use GTFS schedule trips, stop_times, shapes, and stops to estimate whether corridor segments have scheduled frequencies of 15 minutes or less. - - - Estimated High Quality Transit Areas as described in Public Resources Code 21155, 21064.3, 21060.2. - - - - completed - - - - - - - monthly - - - - - - - - transportation - - - - theme - - - - - - - - Downloadable Data - - - - - - - - Public. - - - - - - - The data are made available to the public solely for informational purposes. Information provided in the Caltrans GIS Data Library is accurate to the best of our knowledge and is subject to change on a regular basis, without notice. While Caltrans makes every effort to provide useful and accurate information, we do not warrant the information Use Limitation - The data are made available to the public solely for informational purposes. Information provided in the Caltrans GIS Data Library is accurate to the best of our knowledge and is subject to change on a regular basis, without notice. While Caltrans makes every effort to provide useful and accurate information, we do not warrant the information to be authoritative, complete, factual, or timely. Information is provided on an 'as is' and an 'as available' basis. The Department of Transportation is not liable to any party for any cost or damages, including any direct, indirect, special, incidental, or consequential damages, arising out of or in connection with the access or use of, or the inability to access or use, the Site or any of the Materials or Services described herein. - - - - - - - Creative Commons 4.0 Attribution. - - - - license - - - - - - - vector - - - - - eng - - - - - utf8 - - - - transportation - - - Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 12.9.2.32739 - - - - - California - - - - - - - - - - - File Geodatabase Feature Class - - - - - - - - - - https://github.com/cal-itp/data-analyses/blob/main/high_quality_transit_areas/README.md - - - Readme - - - This site allows you to access the code used to create this dataset and provides additional explanatory resources. - - - - information - - - - - - - - - - - - - - - feature - - - - - - - - 4 meters - - - - - - - - - - This data was estimated using a spatial process derived from General Transit Feed Specification (GTFS) schedule data. To find high-quality bus corridors, we split each corridor into 1,500 meter segments and counted frequencies at the stop within that segment with the highest number of transit trips. If that stop saw at least 4 trips per hour for at least one hour in the morning, and again for at least one hour in the afternoon, we consider that segment a high-quality bus corridor. Segments without a stop are not considered high-quality corridors. Major transit stops were identified as either the intersection of two high-quality corridors from the previous step, a rail or bus rapid transit station, or a ferry terminal with bus service. Note that the definition of `bus rapid transit` in Public Resources Code 21060.2 includes features not captured by available data sources, these features were captured manually using information from transit agency sources and imagery. We believe this data to be broadly accurate, and fit for purposes including overall dashboards, locating facilities in relation to high quality transit areas, and assessing community transit coverage. However, the spatial determination of high-quality transit areas from GTFS data necessarily involves some assumptions as described above. Any critical determinations of whether a specific parcel is located within a high-quality transit area should be made in conjunction with local sources, such as transit agency timetables. Notes: Null values may be present. The `hqta_details` columns defines which part of the Public Resources Code definition the HQTA classification was based on. If `hqta_details` references a single operator, then `agency_secondary` and `base64_url_secondary` are null. If `hqta_details` references the same operator, then `agency_secondary` and `base64_url_secondary` are the same as `agency_primary` and `base64_url_primary`. - - - - - - - - \ No newline at end of file + + + +eng + + +utf8 + + +dataset + + +dataset + + + + +2024-12-19 + + +ISO 19139 Geographic Information - Metadata - Implementation Specification + + +2007 + + + + +geometryOnly + + + + +composite + + + + + + + + + + +4326 + + +EPSG + + +6.2(3.0.1) + + + + + + + + + + +<gco:CharacterString>ca_hq_transit_areas</gco:CharacterString> + + + + +mapDigital + + + + +Use GTFS schedule trips, stop_times, shapes, and stops to estimate whether corridor segments have scheduled frequencies of 15 minutes or less. + + +Estimated High Quality Transit Corridors and Major Transit Stops as described in Public Resources Code 21155, 21064.3, 21060.2. + + + + +Transportation + + +Land Use + + +Transit-Oriented Development + + +TOD + + +High Quality Transit + + + + + + +Public. + + + + +vector + + +eng + + +utf8 + + +Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 12.9.12.32739 + + + + + + + + +File Geodatabase Feature Class + + + + + + + + diff --git a/open_data/xml/ca_hq_transit_stops.xml b/open_data/xml/ca_hq_transit_stops.xml index e171b5047..b04c34894 100644 --- a/open_data/xml/ca_hq_transit_stops.xml +++ b/open_data/xml/ca_hq_transit_stops.xml @@ -1,307 +1,130 @@ - - - - - eng - - - - - utf8 - - - - - dataset - - - - dataset - - - - 2024-10-30 - - - ISO 19139 Geographic Information - Metadata - Implementation Specification - - - 2007 - - - - - - geometryOnly - - - - - - - point - - - - - - - - - - - - 4326 - - - EPSG - - - 6.2(3.0.1) - - - - - - - - - - - ca_hq_transit_stops - - - - - 2022-02-08 - - - - creation - - - - - - - - 2024-10-16 - - - - revision - - - - - - - - Caltrans - - - - publisher - - - - - - - - Eric Dasmalchi - - - Caltrans - - - Data & Digital Services / California Integrated Travel Project - - - - - - - eric.dasmalchi@dot.ca.gov - - - - - - - - pointOfContact - - - - - - - mapDigital - - - - - - Use GTFS schedule trips, stop_times, shapes, and stops to estimate whether corridor segments have scheduled frequencies of 15 minutes or less. - - - Estimated stops along High Quality Transit Corridors, plus major transit stops for bus rapid transit, ferry, rail modes as described in Public Resources Code 21155, 21064.3, 21060.2. - - - - completed - - - - - - - monthly - - - - - - - - transportation - - - - theme - - - - - - - - Downloadable Data - - - - - - - - Public. - - - - - - - The data are made available to the public solely for informational purposes. Information provided in the Caltrans GIS Data Library is accurate to the best of our knowledge and is subject to change on a regular basis, without notice. While Caltrans makes every effort to provide useful and accurate information, we do not warrant the information Use Limitation - The data are made available to the public solely for informational purposes. Information provided in the Caltrans GIS Data Library is accurate to the best of our knowledge and is subject to change on a regular basis, without notice. While Caltrans makes every effort to provide useful and accurate information, we do not warrant the information to be authoritative, complete, factual, or timely. Information is provided on an 'as is' and an 'as available' basis. The Department of Transportation is not liable to any party for any cost or damages, including any direct, indirect, special, incidental, or consequential damages, arising out of or in connection with the access or use of, or the inability to access or use, the Site or any of the Materials or Services described herein. - - - - - - - Creative Commons 4.0 Attribution. - - - - license - - - - - - - vector - - - - - eng - - - - - utf8 - - - - transportation - - - Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 12.9.2.32739 - - - - - California - - - - - - - - - - - File Geodatabase Feature Class - - - - - - - - - - https://github.com/cal-itp/data-analyses/blob/main/high_quality_transit_areas/README.md - - - Readme - - - This site allows you to access the code used to create this dataset and provides additional explanatory resources. - - - - information - - - - - - - - - - - - - - - feature - - - - - - - - 4 meters - - - - - - - - - - This data was estimated using a spatial process derived from General Transit Feed Specification (GTFS) schedule data. To find high-quality bus corridors, we split each corridor into 1,500 meter segments and counted frequencies at the stop within that segment with the highest number of transit trips. If that stop saw at least 4 trips per hour for at least one hour in the morning, and again for at least one hour in the afternoon, we consider that segment a high-quality bus corridor. Segments without a stop are not considered high-quality corridors. Major transit stops were identified as either the intersection of two high-quality corridors from the previous step, a rail or bus rapid transit station, or a ferry terminal with bus service. Note that the definition of `bus rapid transit` in Public Resources Code 21060.2 includes features not captured by available data sources, these features were captured manually using information from transit agency sources and imagery. We believe this data to be broadly accurate, and fit for purposes including overall dashboards, locating facilities in relation to high quality transit areas, and assessing community transit coverage. However, the spatial determination of high-quality transit areas from GTFS data necessarily involves some assumptions as described above. Any critical determinations of whether a specific parcel is located within a high-quality transit area should be made in conjunction with local sources, such as transit agency timetables. Notes: Null values may be present. The `hqta_details` columns defines which part of the Public Resources Code definition the HQTA classification was based on. If `hqta_details` references a single operator, then `agency_secondary` and `base64_url_secondary` are null. If `hqta_details` references the same operator, then `agency_secondary` and `base64_url_secondary` are the same as `agency_primary` and `base64_url_primary`. - - - - - - - - \ No newline at end of file + + + +eng + + +utf8 + + +dataset + + +dataset + + + + +2024-12-19 + + +ISO 19139 Geographic Information - Metadata - Implementation Specification + + +2007 + + + + +geometryOnly + + + + +point + + + + + + + + + + +4326 + + +EPSG + + +6.2(3.0.1) + + + + + + + + + + +<gco:CharacterString>ca_hq_transit_stops</gco:CharacterString> + + + + +mapDigital + + + + +Use GTFS schedule trips, stop_times, shapes, and stops to estimate whether corridor segments have scheduled frequencies of 15 minutes or less. + + +Estimated stops along High Quality Transit Corridors, plus major transit stops for bus rapid transit, ferry, rail modes as described in Public Resources Code 21155, 21064.3, 21060.2. + + + + +Transportation + + +Land Use + + +Transit-Oriented Development + + +TOD + + +High Quality Transit + + + + + + +Public. + + + + +vector + + +eng + + +utf8 + + +Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 12.9.12.32739 + + + + + + + + +File Geodatabase Feature Class + + + + + + + + diff --git a/open_data/xml/ca_transit_routes.xml b/open_data/xml/ca_transit_routes.xml index fb80aaaea..2f7ce06d8 100644 --- a/open_data/xml/ca_transit_routes.xml +++ b/open_data/xml/ca_transit_routes.xml @@ -15,7 +15,7 @@ -2024-11-18 +2024-12-19 ISO 19139 Geographic Information - Metadata - Implementation Specification diff --git a/open_data/xml/ca_transit_stops.xml b/open_data/xml/ca_transit_stops.xml index b9f5826dc..a5e5491d7 100644 --- a/open_data/xml/ca_transit_stops.xml +++ b/open_data/xml/ca_transit_stops.xml @@ -15,7 +15,7 @@ -2024-11-18 +2024-12-19 ISO 19139 Geographic Information - Metadata - Implementation Specification diff --git a/open_data/xml/speeds_by_route_time_of_day.xml b/open_data/xml/speeds_by_route_time_of_day.xml index b75a2356b..9386ed65f 100644 --- a/open_data/xml/speeds_by_route_time_of_day.xml +++ b/open_data/xml/speeds_by_route_time_of_day.xml @@ -15,7 +15,7 @@ -2024-11-18 +2024-12-19 ISO 19139 Geographic Information - Metadata - Implementation Specification diff --git a/open_data/xml/speeds_by_stop_segments.xml b/open_data/xml/speeds_by_stop_segments.xml index a47438d65..8c148b9c7 100644 --- a/open_data/xml/speeds_by_stop_segments.xml +++ b/open_data/xml/speeds_by_stop_segments.xml @@ -15,7 +15,7 @@ -2024-11-18 +2024-12-19 ISO 19139 Geographic Information - Metadata - Implementation Specification diff --git a/portfolio/rt/district_01-eureka/00__01_new_speedmaps__district_01-eureka__organization_source_record_id_recaaoqedvwhcmivt.ipynb b/portfolio/rt/district_01-eureka/00__01_new_speedmaps__district_01-eureka__organization_source_record_id_recaaoqedvwhcmivt.ipynb index 9e0e4f886..62e6c8695 100644 --- 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b/portfolio/rt/district_11-san-diego/03__01_new_speedmaps__district_11-san-diego__organization_source_record_id_reczalk4vysuotvjf.ipynb @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:9d3df322b047086acd2fd3e9a70929927463190e8b0d3dba480413ea4146eb50 -size 63741 +oid sha256:a4b71348001d314d2e68885aed3f0e81c15baebffdf32c82466944c3d66ed116 +size 63706 diff --git a/portfolio/rt/district_12-irvine/00__01_new_speedmaps__district_12-irvine__organization_source_record_id_recfma7gnr5lqtttg.ipynb b/portfolio/rt/district_12-irvine/00__01_new_speedmaps__district_12-irvine__organization_source_record_id_recfma7gnr5lqtttg.ipynb index 14f219f56..61e675dbc 100644 --- a/portfolio/rt/district_12-irvine/00__01_new_speedmaps__district_12-irvine__organization_source_record_id_recfma7gnr5lqtttg.ipynb +++ b/portfolio/rt/district_12-irvine/00__01_new_speedmaps__district_12-irvine__organization_source_record_id_recfma7gnr5lqtttg.ipynb @@ -1,3 +1,3 @@ version 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b/rt_scheduled_v_ran/logs/rt_v_scheduled_route_metrics.log @@ -78,3 +78,5 @@ 2024-10-23 09:50:16.036 | INFO | __main__:route_metrics:85 - route aggregation 2024-10-19: 0:00:01.776961 2024-10-23 09:50:18.482 | INFO | __main__:route_metrics:85 - route aggregation 2024-10-20: 0:00:02.442012 2024-11-20 10:54:08.184 | INFO | __main__:route_metrics:85 - route aggregation 2024-11-13: 0:00:05.430277 +2024-12-17 15:40:02.618 | INFO | __main__:route_metrics:85 - route aggregation 2024-12-11: 0:00:02.999985 +2024-12-19 09:53:33.513 | INFO | __main__:route_metrics:85 - route aggregation 2024-12-11: 0:00:03.161056 diff --git a/rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log b/rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log index 58d3d9d78..f999d3b8d 100644 --- a/rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log +++ b/rt_scheduled_v_ran/logs/rt_v_scheduled_trip_metrics.log @@ -477,3 +477,9 @@ 2024-11-15 14:09:06.185 | INFO | __main__:rt_schedule_trip_metrics:280 - tabular trip metrics 2024-11-13: 0:02:58.980377 2024-11-15 14:33:56.434 | INFO | __main__:rt_schedule_trip_metrics:285 - spatial trip metrics 2024-11-13: 0:24:50.248289 2024-11-15 14:35:23.497 | INFO | __main__:rt_schedule_trip_metrics:333 - Total run time for metrics on 2024-11-13: 0:29:16.291628 +2024-12-17 15:14:01.357 | INFO | __main__:rt_schedule_trip_metrics:280 - tabular trip metrics 2024-12-11: 0:02:50.826842 +2024-12-17 15:38:16.518 | INFO | __main__:rt_schedule_trip_metrics:285 - spatial trip metrics 2024-12-11: 0:24:15.160640 +2024-12-17 15:39:42.245 | INFO | __main__:rt_schedule_trip_metrics:333 - Total run time for metrics on 2024-12-11: 0:28:31.715344 +2024-12-19 09:26:56.119 | INFO | __main__:rt_schedule_trip_metrics:280 - tabular trip metrics 2024-12-11: 0:03:23.206651 +2024-12-19 09:51:46.617 | INFO | __main__:rt_schedule_trip_metrics:285 - spatial trip metrics 2024-12-11: 0:24:50.497597 +2024-12-19 09:53:04.131 | INFO | __main__:rt_schedule_trip_metrics:333 - Total run time for metrics on 2024-12-11: 0:29:31.218478 diff --git a/rt_scheduled_v_ran/scripts/update_vars.py b/rt_scheduled_v_ran/scripts/update_vars.py index 33fb02031..11f5f64ff 100644 --- a/rt_scheduled_v_ran/scripts/update_vars.py +++ b/rt_scheduled_v_ran/scripts/update_vars.py @@ -7,7 +7,7 @@ oct2024_week = rt_dates.get_week("oct2024", exclude_wed=True) -analysis_date_list = [rt_dates.DATES["nov2024"]] +analysis_date_list = [rt_dates.DATES["dec2024"]] # analysis_date_list = rt_dates.y2024_dates + rt_dates.y2023_dates GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data") diff --git a/rt_segment_speeds/42_switch_vp_nn_file.ipynb b/rt_segment_speeds/42_switch_vp_nn_file.ipynb index bb4d06584..f8b3af1aa 100644 --- a/rt_segment_speeds/42_switch_vp_nn_file.ipynb +++ b/rt_segment_speeds/42_switch_vp_nn_file.ipynb @@ -25,7 +25,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, + "id": "dac3e373-6661-45a4-8884-033858be7118", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('./scripts')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "id": "6ad4dd53-bcdc-4fbf-ab6b-5073101086e1", "metadata": {}, "outputs": [], @@ -37,6 +48,236 @@ "from shared_utils import rt_dates" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7733d41a-1bba-48f9-8dfa-c56e5d8b93f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'dir': '${gcs_paths.SEGMENT_GCS}', 'stage1': '${speeds_tables.vp_dwell}', 'stage2': 'nearest/nearest_vp_shape_segments', 'stage3': 'stop_arrivals', 'stage4': 'speeds_stop_segments', 'trip_stop_cols': ['trip_instance_key', 'stop_sequence'], 'shape_stop_cols': ['shape_array_key', 'shape_id', 'stop_sequence'], 'stop_pair_cols': ['stop_pair', 'stop_pair_name'], 'route_dir_cols': ['route_id', 'direction_id'], 'segment_cols': ['route_id', 'direction_id', 'stop_pair', 'geometry'], 'route_dir_single_segment': 'rollup_singleday/speeds_route_dir_segments', 'route_dir_single_segment_detail': 'rollup_singleday/speeds_route_dir_segments_detail', 'route_dir_multi_segment': 'rollup_multiday/speeds_route_dir_segments', 'segments_file': 'segment_options/shape_stop_segments', 'max_speed': '${speed_vars.max_speed}', 'route_dir_quarter_segment': 'rollup_multiday/quarter_speeds_route_dir_segments', 'route_dir_year_segment': 'rollup_multiday/year_speeds_route_dir_segments'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GTFS_DATA_DICT['stop_segments']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "278a3539-3bf0-4277-a552-07632368af49", + "metadata": {}, + "outputs": [], + "source": [ + "analysis_date = rt_dates.DATES['dec2024']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b0ebe07b-5cc5-4f8f-bbe1-220e097d05fe", + "metadata": {}, + "outputs": [], + "source": [ + "STOP_ARRIVALS_FILE = f\"{GTFS_DATA_DICT['stop_segments']['stage3']}_{analysis_date}.parquet\"" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3586ae05-7fa8-4527-a242-a72a841cd6f7", + "metadata": {}, + "outputs": [], + "source": [ + "path = f\"{GTFS_DATA_DICT['stop_segments']['dir']}{GTFS_DATA_DICT['stop_segments']['stage3']}_{analysis_date}.parquet\"" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "81b52f4c-c0bf-44c8-8bfa-f32ac6a573d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trip_instance_keystop_sequenceshape_array_keystop_metersarrival_time
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..................
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\n", + "

2835752 rows × 5 columns

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" + ], + "text/plain": [ + " trip_instance_key stop_sequence \\\n", + "0 0001b91f4f2c8ace7b3ebbefd5e9465b 1 \n", + "1 0001b91f4f2c8ace7b3ebbefd5e9465b 2 \n", + "2 0001b91f4f2c8ace7b3ebbefd5e9465b 3 \n", + "3 0001b91f4f2c8ace7b3ebbefd5e9465b 4 \n", + "4 0001b91f4f2c8ace7b3ebbefd5e9465b 5 \n", + "... ... ... \n", + "2835747 ffffec76acad6484662b190b61b3ae0a 21 \n", + "2835748 ffffec76acad6484662b190b61b3ae0a 22 \n", + "2835749 ffffec76acad6484662b190b61b3ae0a 23 \n", + "2835750 ffffec76acad6484662b190b61b3ae0a 24 \n", + "2835751 ffffec76acad6484662b190b61b3ae0a 25 \n", + "\n", + " shape_array_key stop_meters arrival_time \n", + "0 b1e9813cd5549488c1770dd31bfcc739 506.474524 2024-12-11 19:02:41 \n", + "1 b1e9813cd5549488c1770dd31bfcc739 777.366754 2024-12-11 19:03:30 \n", + "2 b1e9813cd5549488c1770dd31bfcc739 1053.968008 2024-12-11 19:03:57 \n", + "3 b1e9813cd5549488c1770dd31bfcc739 1383.761588 2024-12-11 19:04:31 \n", + "4 b1e9813cd5549488c1770dd31bfcc739 1783.021004 2024-12-11 19:05:36 \n", + "... ... ... ... \n", + "2835747 f19bb4d136c8de91ebd1fc8471378ac5 11212.146566 2024-12-11 16:33:12 \n", + "2835748 f19bb4d136c8de91ebd1fc8471378ac5 11431.597845 2024-12-11 16:34:28 \n", + "2835749 f19bb4d136c8de91ebd1fc8471378ac5 11666.768633 2024-12-11 16:34:54 \n", + "2835750 f19bb4d136c8de91ebd1fc8471378ac5 12007.971821 2024-12-11 16:36:21 \n", + "2835751 f19bb4d136c8de91ebd1fc8471378ac5 12340.666901 2024-12-11 16:39:18 \n", + "\n", + "[2835752 rows x 5 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.read_parquet(path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2fa6519-758a-4f23-967c-e29ab7026a96", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_parquet(\n", + " f\"{SEGMENT_GCS}{STOP_ARRIVALS_FILE}.parquet\"\n", + " )" + ] + }, { "cell_type": "code", "execution_count": 11, @@ -486,6 +727,13 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/rt_segment_speeds/logs/avg_speeds.log b/rt_segment_speeds/logs/avg_speeds.log index 4f75d4755..abffd9563 100644 --- a/rt_segment_speeds/logs/avg_speeds.log +++ b/rt_segment_speeds/logs/avg_speeds.log @@ -578,3 +578,9 @@ 2024-11-25 21:14:33.232 | INFO | average_segment_speeds:segment_averages_detail:247 - speedmap_segments detailed segment averaging for ['2024-07-17'] execution time: 0:05:37.386735 2024-11-25 21:19:21.360 | INFO | average_segment_speeds:segment_averages:183 - speedmap_segments segment averaging for ['2024-07-17'] execution time: 0:04:47.990211 2024-11-25 21:23:05.965 | INFO | average_segment_speeds:segment_averages:183 - speedmap_segments segment averaging for ['2024-07-17'] execution time: 0:03:44.478500 +2024-12-18 13:51:03.723 | INFO | __main__:segment_averages:185 - stop_segments segment averaging for ['2024-12-11'] execution time: 0:04:49.205336 +2024-12-18 14:45:36.844 | INFO | __main__:summary_average_speeds:120 - trip avg 0:00:44.679926 +2024-12-18 14:45:50.813 | INFO | __main__:summary_average_speeds:155 - rt_stop_times summary speed averaging for ['2024-12-11'] execution time: 0:00:58.649483 +2024-12-18 15:06:31.961 | INFO | average_segment_speeds:segment_averages_detail:249 - speedmap_segments detailed segment averaging for ['2024-12-11'] execution time: 0:07:17.222047 +2024-12-18 15:12:50.801 | INFO | average_segment_speeds:segment_averages:185 - speedmap_segments segment averaging for ['2024-12-11'] execution time: 0:06:18.665542 +2024-12-18 15:17:58.646 | INFO | average_segment_speeds:segment_averages:185 - speedmap_segments segment averaging for ['2024-12-11'] execution time: 0:05:07.636255 diff --git a/rt_segment_speeds/logs/cut_stop_segments.log b/rt_segment_speeds/logs/cut_stop_segments.log index e226db484..325748b96 100644 --- a/rt_segment_speeds/logs/cut_stop_segments.log +++ b/rt_segment_speeds/logs/cut_stop_segments.log @@ -69,3 +69,7 @@ 2024-11-25 16:52:05.319 | INFO | __main__::244 - speedmap segments and proxy_stop_times 2024-04-17: 0:04:28.728100 2024-11-25 19:29:33.128 | INFO | __main__::155 - cut segments 2024-07-17: 0:19:53.239259 2024-11-25 19:35:15.706 | INFO | __main__::244 - speedmap segments and proxy_stop_times 2024-07-17: 0:04:18.392759 +2024-12-17 16:56:06.891 | INFO | __main__::168 - cut segments 2024-12-11: 0:23:07.227761 +2024-12-17 17:03:15.346 | INFO | __main__::244 - speedmap segments and proxy_stop_times 2024-12-11: 0:05:24.205473 +2024-12-18 09:06:06.283 | INFO | __main__::168 - cut segments 2024-12-11: 0:25:27.680094 +2024-12-18 09:13:13.266 | INFO | __main__::244 - speedmap segments and proxy_stop_times 2024-12-11: 0:04:55.428371 diff --git a/rt_segment_speeds/logs/interpolate_stop_arrival.log b/rt_segment_speeds/logs/interpolate_stop_arrival.log index 086444c22..41ff1addc 100644 --- a/rt_segment_speeds/logs/interpolate_stop_arrival.log +++ b/rt_segment_speeds/logs/interpolate_stop_arrival.log @@ -133,3 +133,7 @@ 2024-11-25 20:11:30.072 | INFO | interpolate_stop_arrival:interpolate_stop_arrivals:279 - interpolate arrivals for stop_segments 2024-07-17: 2024-07-17: 0:14:23.629904 2024-11-25 20:55:50.314 | INFO | interpolate_stop_arrival:interpolate_stop_arrivals:279 - interpolate arrivals for rt_stop_times 2024-07-17: 2024-07-17: 0:13:51.485049 2024-11-25 21:06:50.404 | INFO | interpolate_stop_arrival:interpolate_stop_arrivals:279 - interpolate arrivals for speedmap_segments 2024-07-17: 2024-07-17: 0:02:33.588358 +2024-12-17 17:43:47.305 | INFO | interpolate_stop_arrival:interpolate_stop_arrivals:279 - interpolate arrivals for stop_segments 2024-12-11: 2024-12-11: 0:14:09.409333 +2024-12-18 10:52:54.623 | INFO | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for stop_segments 2024-12-11: 2024-12-11: 0:10:20.061296 +2024-12-18 14:42:51.535 | INFO | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for rt_stop_times 2024-12-11: 2024-12-11: 0:10:33.922215 +2024-12-18 14:56:54.836 | INFO | interpolate_stop_arrival:interpolate_stop_arrivals:236 - interpolate arrivals for speedmap_segments 2024-12-11: 2024-12-11: 0:02:36.918147 diff --git a/rt_segment_speeds/logs/nearest_vp.log b/rt_segment_speeds/logs/nearest_vp.log index 56c0d1d14..8489eee65 100644 --- a/rt_segment_speeds/logs/nearest_vp.log +++ b/rt_segment_speeds/logs/nearest_vp.log @@ -271,3 +271,8 @@ 2024-11-25 21:00:54.275 | INFO | nearest_vp_to_stop:nearest_neighbor_for_stop:178 - nearest neighbor for speedmap_segments 2024-07-17: 0:02:16.592519 2024-11-25 21:04:16.776 | INFO | vp_around_stops:filter_to_nearest_two_vp:248 - nearest 2 vp for speedmap_segments 2024-07-17: 0:03:22.103136 2024-11-27 13:35:33.184 | INFO | __main__:nearest_neighbor_for_stop:66 - nearest neighbor for stop_segments 2024-10-16: 0:14:44.207350 +2024-12-17 17:18:50.557 | INFO | nearest_vp_to_stop:nearest_neighbor_for_stop:178 - nearest neighbor for stop_segments 2024-12-11: 0:15:15.814696 +2024-12-17 17:29:37.837 | INFO | vp_around_stops:filter_to_nearest_two_vp:248 - nearest 2 vp for stop_segments 2024-12-11: 0:10:45.231077 +2024-12-18 10:42:34.461 | INFO | nearest_vp_to_stop:nearest_neighbor_for_stop:189 - nearest neighbor for stop_segments 2024-12-11: 0:36:50.316084 +2024-12-18 14:32:17.520 | INFO | nearest_vp_to_stop:nearest_neighbor_for_stop:189 - nearest neighbor for rt_stop_times 2024-12-11: 0:40:52.717197 +2024-12-18 14:54:17.879 | INFO | nearest_vp_to_stop:nearest_neighbor_for_stop:189 - nearest neighbor for speedmap_segments 2024-12-11: 0:08:09.784862 diff --git a/rt_segment_speeds/logs/speeds_by_segment_trip.log b/rt_segment_speeds/logs/speeds_by_segment_trip.log index 4b4df02b6..fc97d8640 100644 --- a/rt_segment_speeds/logs/speeds_by_segment_trip.log +++ b/rt_segment_speeds/logs/speeds_by_segment_trip.log @@ -145,3 +145,7 @@ 2024-11-25 20:13:05.965 | INFO | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for stop_segments 2024-07-17: 0:01:35.820370 2024-11-25 20:57:30.167 | INFO | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for rt_stop_times 2024-07-17: 0:01:39.810442 2024-11-25 21:08:39.293 | INFO | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for speedmap_segments 2024-07-17: 0:01:39.807326 +2024-12-17 17:45:32.983 | INFO | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for stop_segments 2024-12-11: 0:01:45.602657 +2024-12-18 10:54:39.517 | INFO | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for stop_segments 2024-12-11: 0:01:44.792095 +2024-12-18 14:44:34.009 | INFO | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for rt_stop_times 2024-12-11: 0:01:42.393123 +2024-12-18 14:58:57.031 | INFO | stop_arrivals_to_speed:calculate_speed_from_stop_arrivals:176 - speeds by segment for speedmap_segments 2024-12-11: 0:01:51.369378 diff --git a/rt_segment_speeds/scripts/average_segment_speeds.py b/rt_segment_speeds/scripts/average_segment_speeds.py index 313426798..0a9fea9e0 100644 --- a/rt_segment_speeds/scripts/average_segment_speeds.py +++ b/rt_segment_speeds/scripts/average_segment_speeds.py @@ -55,7 +55,7 @@ def concatenate_trip_segment_speeds( SPEED_FILE = dict_inputs["stage4"] MAX_SPEED = dict_inputs["max_speed"] - + df = time_series_utils.concatenate_datasets_across_dates( SEGMENT_GCS, SPEED_FILE, @@ -66,14 +66,16 @@ def concatenate_trip_segment_speeds( ROUTE_DIR_COLS + STOP_PAIR_COLS + [ "trip_instance_key", "speed_mph", "meters_elapsed", "sec_elapsed", - "time_of_day"]), + "time_of_day", "arrival_time"]), filters = [[("speed_mph", "<=", MAX_SPEED)]], **kwargs ).pipe( gtfs_schedule_wrangling.add_peak_offpeak_column + ) + df = df.rename(columns={'arrival_time':'service_date'} ).pipe( gtfs_schedule_wrangling.add_weekday_weekend_column - ) + ) # drop service_date? print("concatenated files") return df @@ -267,7 +269,7 @@ def segment_averages_detail( ROUTE_DIR_COLS = [*dict_inputs["route_dir_cols"]] STOP_PAIR_COLS = [*dict_inputs["stop_pair_cols"]] - TIME_OF_DAY_FILE = dict_inputs["shape_stop_single_segment"] + "_test" +# TIME_OF_DAY_FILE = dict_inputs["shape_stop_single_segment"] + "_test" ROUTE_SEG_FILE = dict_inputs["route_dir_single_segment"] for analysis_date in analysis_date_list: @@ -280,14 +282,14 @@ def segment_averages_detail( weighted_averages = True ) - segment_averages( - [analysis_date], - segment_type, - group_cols = (OPERATOR_COLS + ROUTE_DIR_COLS + - STOP_PAIR_COLS + ["time_of_day"]), - export_file = TIME_OF_DAY_FILE, - weighted_averages = False - ) +# segment_averages( +# [analysis_date], +# segment_type, +# group_cols = (OPERATOR_COLS + ROUTE_DIR_COLS + +# STOP_PAIR_COLS + ["time_of_day"]), +# export_file = TIME_OF_DAY_FILE, +# weighted_averages = False +# ) ''' from segment_speed_utils.project_vars import weeks_available diff --git a/rt_segment_speeds/segment_speed_utils/neighbor.py b/rt_segment_speeds/segment_speed_utils/neighbor.py index 2e3f9a307..ad08361e8 100644 --- a/rt_segment_speeds/segment_speed_utils/neighbor.py +++ b/rt_segment_speeds/segment_speed_utils/neighbor.py @@ -179,7 +179,7 @@ def two_nearest_neighbor_near_stop( ) before_vp, after_vp, before_meters, after_meters = filter_to_nearest2_vp( - valid_vp_idx_array[nearest_indices], # subset of coords in nn + valid_vp_coords_array[nearest_indices], # subset of coords in nn shape_geometry, valid_vp_idx_array[nearest_indices], # subset of vp_idx in nn stop_meters, diff --git a/rt_segment_speeds/segment_speed_utils/project_vars.py b/rt_segment_speeds/segment_speed_utils/project_vars.py index 83bf9ddb1..6d28427eb 100644 --- a/rt_segment_speeds/segment_speed_utils/project_vars.py +++ b/rt_segment_speeds/segment_speed_utils/project_vars.py @@ -11,7 +11,7 @@ SHARED_GCS = GTFS_DATA_DICT.gcs_paths.SHARED_GCS PUBLIC_GCS = GTFS_DATA_DICT.gcs_paths.PUBLIC_GCS -analysis_date = rt_dates.DATES["jul2024"] +analysis_date = rt_dates.DATES["dec2024"] oct2023_week = rt_dates.get_week("oct2023", exclude_wed=True) apr2023_week = rt_dates.get_week("apr2023", exclude_wed=True)