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Better Representation of the Pedestrian Environment in Travel Demand Models
Principal Investigator:
Kelly Clifton, Portland State University
Co-Investigators:
- Robert Schneider, University of California, Berkeley
- Jennifer Toole, Toole Design Group
Summary:
There is growing support for improvements to the quality of the walking environment, including more investments to promote pedestrian travel. Metropolitan planning organizations (MPOs) are improving regional travel demand forecasting models to better represent walking and bicycling and to expand the evaluative capacity of models to address policy-relevant issues like air quality, public health, an...
There is growing support for improvements to the quality of the walking environment, including more investments to promote pedestrian travel. Metropolitan planning organizations (MPOs) are improving regional travel demand forecasting models to better represent walking and bicycling and to expand the evaluative capacity of models to address policy-relevant issues like air quality, public health, and the smart allocation of infrastructure and other resources. This report describes an innovative, spatially disaggregate method to integrate walking activity into trip-based travel models. Using data for the Portland, OR, metropolitan area, the method applies trip generation at a new micro-scale spatial unit: a 264-foot-by-264-foot (80-meters-by-80-meters) pedestrian analysis zone (PAZ). Next, a binary logit walk mode split model—using a new pedestrian environment measure—estimates the number of walk trips generated. Non-walk trips are then aggregated up to larger transportation analysis zones (TAZs) for destination choice, mode choice, and traffic assignment. Finally, there are opportunities for choosing destinations and for routing of the PAZ pedestrian trips. This method improves travel models’ sensitivity to policy- and investment-related walking influences, and it could operate as a standalone tool for rapid scenario analysis. Care must be taken when applying this method with respect to scalability, forecasting, and operational challenges.
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Project Details
| Project Type: | Research |
|---|---|
| Project Status: | Completed |
| End Date: | August 31, 2013 |
| UTC Funding: | $150,000 |