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A Tradesman demo for the TRB Innovations in Travel Modeling conference 2023

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Tradesman Demo

This is a simple Tradesman demo project presented at the 2023 TRB Innovations in Travel Modelling conference in Indianapolis, Indiana USA between June 3rd and June 6th.

It consists of a single Jupyter Notebook that has been tested with Python 3.10 running on Windows 11 and on Google Colab

Our [webpage for this conference(http://itm2023.outerloop.io) contains references to all resources presented at ITM 2023.

Running on Windows

The setup below assumes you have Python and Windows Terminal installed.

On Windows Terminal:

git clone https://github.com/outerl/tradesman-demo.git
cd tradesman-demo

python -m pip install virtualenv

python -m virtualenv .venv
.\.venv\Scripts\activate.ps1

python -m install -r requirements.txt
jupyter lab

Running on Google Colab

You can just jump straight into Google Colab to run this tutorial notebook in the cloud.

Open in Colab

Please note that, when using the free version of Google Colab, you are not guaranteed to run the entire notebook before shutting down.

Indiana example

For those interested in looking at the results of a more substantial example, a sample model for Indiananapolis was also prepared and is available for DOWNLOAD.

The Indianapolis example provided in the tutorial was generated on 30/05/2023 with the code below

import os
os.environ['USE_PYGEOS'] = '0'

from uuid import uuid4
from tradesman.model import Tradesman
from tempfile import gettempdir

model_place = "Indianapolis, Indiana USA"
folder = os.path.join(gettempdir(), "Indianapolis")

model = Tradesman(network_path=folder, model_place=model_place)

model.import_model_area()
model.add_country_borders()
model.import_subdivisions(subdivision_levels=2, overwrite=True)
model.import_network()
model.import_population()
model.build_zoning(hexbin_size=200, min_zone_pop=250, max_zone_pop=2000)
model.import_pop_by_sex_and_age()
model.import_amenities()
model.import_buildings(download_from_bing=False)
model.build_population_synthesizer_data(sample_size=0.02)
model.synthesize_population()

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