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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


Xuan Lu

Song Gao

Eran Ben-Elia

Ryan Pothering


© 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.


Lu, X., Gao, S., Ben-Elia, E., & Pothering, R. (2014). Travelers' day-to-day route choice behavior with real-time information in a congested risky network. Mathematical Population Studies, 21(4), 205-219.

Journal Article Type Article
Publication Date Jan 1, 2014
Journal Mathematical Population Studies
Print ISSN 0889-8480
Publisher Taylor & Francis (Routledge)
Peer Reviewed Peer Reviewed
Volume 21
Issue 4
Pages 205-219
Keywords experiment, uncertain network, reinforcement learning, real-time information
Public URL
Publisher URL
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:


LuGaoBenEliaPothering2011_rep.docx (94 Kb)

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