A multi-class, multi-criteria bicycle traffic assignment model

Abstract

Cycling is gaining popularity both as a mode of travel in urban communities and as an alternative mode to private motorized vehicles due to its wide range of benefits (health, environmental, and economical). However, this change in modal share is not reflected in current transportation planning and travel demand forecasting modeling processes. The existing practices to model bicycle trips in a network are not sophisticated enough to describe the full cyclist experience in route decision-making. This is evident in the existing practices’ methodology: the all-or-nothing assignment uses single attributes such as distance, safety, or a composite measure of safety multiplied by distance. The purpose of this article is to develop a multi-class and multi-criteria bicycle traffic assignment model that not only accounts for multiple user classes by acknowledging that there are different types of cyclists with varying levels of biking experience, but also for relevant factors that may affect each user classes behavior in route choice decisions. The multi-class, multi-criteria bicycle traffic assignment model is developed in a two-stage process. The first stage examines key criteria to generate the set of non-dominated (or efficient) routes for each user class, and the second stage determines the flow allocation to efficient routes by user class. Numerical experiments are then conducted to demonstrate the two-stage approach for the multi-class, multi-criteria bicycle traffic assignment model.

Keywords:

• Bicycle
• cyclist route choice
• multi-class
• multi-objective shortest path
• traffic assignment

Acknowledgments

The authors are grateful to Prof. S.C. Wong (Editor-in-Chief) and four anonymous referees for their constructive comments and suggestions to improve the quality and clarity of the article.

Additional information

Funding

This research was supported by the Research Grants Council (RGC) of the Hong Kong Special Administrative Region (Project No. 15212217), the Hong Kong Scholars Program (Project No. G-YZ1R), the Research Institute for Sustainable Urban Development at the Hong Kong Polytechnic University (1-BBWF), the Ministry of Science, ICT, Republic of Korea (K-20-L01-C06-S01), the Basic Science Research Program through the National Research Foundation of Korea (NRF-2016R1C1B2016254), the Dwight David Eisenhower Transportation Fellowship Program, and the Transportation Research Center for Livable Communities (TRCLC) sponsored by the U.S. Department of Transportation. These supports are gratefully acknowledged.