Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/102462
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Title: A multi-class, multi-criteria bicycle traffic assignment model
Authors: Ryu, S
Chen, A 
Su, J
Choi, K
Issue Date: 2021
Source: International journal of sustainable transportation, 2021, v. 15, no. 7, p. 524-540
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
Publisher: Taylor & Francis
Journal: International journal of sustainable transportation 
ISSN: 1556-8318
EISSN: 1556-8334
DOI: 10.1080/15568318.2020.1770906
Rights: © 2020 Taylor & Francis Group, LLC
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Sustainable Transportation on 04 Jun 2020 (published online), available at: http://www.tandfonline.com/10.1080/15568318.2020.1770906.
Appears in Collections:Journal/Magazine Article

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