VMT Reduction and Maximum Mode Shift Study

This study estimates the maximum amount of mode shift possible given existing transportation infrastructure, land use, and travel patterns. Unlike travel forecasting models, this project does not model changes to the transportation system, population size, or where people live, work, shop, and travel. By analyzing the current system, the project estimates the baseline potential for behavioral changes alone to reduce vehicle miles traveled and increase the share of trips made by walking, transit, or biking.

Further details and study conclusions available on metrocouncil.org.

Presentations and reports

Item	Link
Final PDF report	link
Presentation to Transportation Policy Plan Advisory Workgroup, 12/16/2022	link
Presentation to Transportation Policy Plan Technical Working Group, 9/14/2023	link

The final PDF report is available - VMT Reduction Mode Shift Final Report.pdf

Repository details

Further details are available in READMEs within each subfolder.

• src Source code for main tools. There are two:
  • routing This tool creates the best car, walk, bike and transit path for each TBI trip.
  • analysis_tool The main analysis tool to calculate and summarize the results.
• data_processing Scripts to process the TBI data. Run these again if there is a new wave of the TBI. Must be run before the analysis_tool.
• data_viz A tool to visualize individual routes and check them for reasonableness.
• reports Several reports documenting the process.

Notebook and environment instructions

The files here are mostly jupyter notebooks running python. The installed packages used for development are stored in environment.yml. You can use conda to set up the environment with the right installs:

conda env create --file environment.yml

Then to activate the environment:

conda activate vmtmodeenv

If you have difficulties solving packages on macOS, try using the Mac-specific environment. You may also need to finesse the

conda env create --file mac-environment.yml
conda activate vmtmodeenv

After installing, if you are on a mac, we need to manually install jupyter notebook:

pip install notebook

After installing the packages, you'll use keyring to set the main directory on our shared drive. See contact information below to get data access.

import keyring
keyring.set_password("msp", "vmt_reduction_dir", <directory>)

Data Management

Non-private and non-proprietary example data are the only data we store in this GitHub repository. The analyses used for this project are based on the Travel Behavior Inventory (TBI) which includes personal information for the respondents, including trip locations. For more information or to request data access, please contact us.

Input Data	Access
Metropolitan Council Household Travel Behavior Inventory	Direct acces provided by Council
OpenStreetMap (OSM) network data	Direct access via python packages
General Transit Feed Specification (GTFS)	Provided by Metro Transit and other transit providers in the Twin Cities region
StreetLight Data speeds and volumes	Accessed using the Metropolitan Council StreetLight subscription via the StreetLight API

Please see our DATA MANAGEMENT PLAN for further details on our data practices for this project and the final report appendices for more information.

nbstripout

To avoid committing any identifiable data to the repository through Jupyter Notebooks outputs, we strip all outputs from notebooks before committing. This needs to be set up on each machine accessing the git repository; this is a git security restriction to prevent arbitrary code execution without user consent when working with an untrusted clone.

Full instructions are available on the nbstripout site. In a nutshell, install nbstripout by running pip install --upgrade nbstripout, and then, within the repository directory, run nbstripout --install. This will remove outputs from notebooks when committing, without modifying your local files.

Funding

This project was completed over 2022 and 2023. The University of Kentucky Research Foundation was selected through a competitive request for proposal process and compensated approximately $130,000 under contract 22P159.

Contacts

Metropolitan Council

• Primary contact: Liz Roten email @eroten
• Jonathan Ehrlich email @JonathanEhrlichMC
• Brandon Whited email @Brandon-Whited

Researchers

• Greg Erhardt email @gregerhardt
• Matthew Wigginton Bhagat-Conway email @mattwigway

Contributors

• Ashley Asmus @ashleyasmus
• Eric Lind @elindie
• Xu Zhang @xzh263
• Richard Donohue @rgdonohue

Code of Conduct

Please note that the mode-shift project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.