New Project (TRCLC 15-10)

TRCLC 15-10: Development of Multi-Class, Multi-Criteria Bicycle Traffic Assignment Models and Solution Algorithms

PI: Anthony Chen, Utah State University

Abstract

Many cities are observing a rise in cycling as an alternative mode of travel to conventional private motorized vehicles because of health, environmental, and economical benefits. However, this change in modal share is not reflected in current transportation planning and travel demand forecasting modeling processes. The current methods are based simply on the all-or-nothing (AON) assignment method using a single attractiveness measure such as distance, safety, or a composite measure of safety multiplied by distance. This is problematic because cyclists travel not only on one route, but on many different routes based on different levels of biking experience and different preferences using different combinations of criteria for selecting a cycle route. The AON simplistic modeling of cyclists’ route choice will affect the bicycle traffic assignment results and may influence investment decisions for bicycle infrastructures. Therefore, it is imperative to incorporate heterogeneous cyclist route choice behaviors in the bicycle traffic assignment model in order to enhance the accuracy of bicycle traffic forecasts.

The purpose of this research is to build on these existing studies to develop bicycle traffic assignment models and solution algorithms for estimating bicycle volumes on a transportation network by explicitly considering multiple user classes and multiple criteria considered by cyclists in determining their route choices. By integrating both the multi-criteria and multi-class components into the model, this research seeks to gain a more comprehensive understanding of cyclist decision making and of bicycle network analysis.