Travelers’ Day-to-Day Route Choice Behavior with Real-Time Information in a Congested Risky Network

Lu, Xuan; Gao, Song; Ben-Elia, Eran; Pothering, Ryan

Abstract

© 2014, Taylor & Francis Group, LLC. Nonrecurring disruptions to traffic systems caused by incidents or adverse conditions can result in uncertain travel times. Real-time information allows travelers to adapt to actual traffic conditions. In a behavior experiment, subjects completed 120 “days” of repeated route choices in a hypothetical, competitive network submitted to random capacity reductions. One scenario provided subjects with real-time information regarding a probable incident and the other did not. A reinforcement learning model with two scale factors, a discounting rate of previous experience and a constant term, is estimated by minimizing the deviation between predicted and observed daily flows. The estimation combines brute force enumeration and a subsequent stochastic approximation method. The prediction over 120 runs has a root mean square error of 1.05 per day per route and a bias of 0.14 per route.

• Journal Article Type: Article
• Publication Date: Jan 1, 2014
• Deposit Date: Aug 29, 2012
• Publicly Available Date: May 3, 2016
• Journal: Mathematical Population Studies
• Print ISSN: 0889-8480
• Electronic ISSN: 1547-724X
• Publisher: Taylor & Francis (Routledge)
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Issue: 4
• Pages: 205-219
• DOI: https://doi.org/10.1080/08898480.2013.836418
• Keywords: experiment, uncertain network, reinforcement learning, real-time information
• Public URL: https://uwe-repository.worktribe.com/output/825441
• Publisher URL: http://dx.doi.org/10.1080/08898480.2013.836418
• Additional Information: Additional Information : This is an Accepted Manuscript of an article published by Taylor & Francis in Mathematical Population Studies on 03 November 2014, available online: http://wwww.tandfonline.com/10.1080/08898480.2013.836418
• Contract Date: May 3, 2016