TRB 2016 Blue Ribbon Committee
16th National Transportation Planning Applications Conference

An Analytical Modeling Tool to Evaluate the Effectiveness of Active Transportation Strategies

Corresponding Author: Jinghua Xu, Fehr & Peers

Presented By: Jinghua Xu, Fehr & Peers


With the current California legislation, such as AB-32, SB-375, and SB-743, the focus of transportation planning is increasingly centered on reducing GHGs, improving equity and health, and achieving long-term sustainable economic growth. Local agencies started to shift priorities toward transit and active transportation in their transportation plans. Active transportation, a multimodal solution to connect people to where they need to go using active modes such as walking, bicycling and taking public transit, becomes an effective form of transportation to reach the goals of regional transportation plan in a sustainable way.

Given that a traditional travel demand model is often not fully responsive to various active transportation policy variables, such as bike lane length or density, an Active Transportation (AT) Tool is proposed to enhance the functionality of a travel demand model by increasing its sensitivity to active transportation investments, which allows for a more dynamic assessment of the costs/benefits that could be achieved by applying active transportation strategies within a community or a region.

By using a standard statistical technique called multinomial logistic regression, an AT Tool is designed as a postprocessor to a travel demand model, focusing on the probability of using the various available modes of travel, including walking and biking, to measure walking and biking trips based upon changes in land use and/or in active transportation facility to better understand VMT reductions associated with active transportation infrastructure improvements. These improvements include adding bike lanes/paths, adding sidewalks, improving the street grid, reducing speeds of roadways, and increasing the density and diversity of land uses, etc. This tool can be seamlessly integrated to a travel demand model’s framework. Case studies for various AT scenarios using this tool will also be included in the presentation.


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