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Which road do I take? A learning-based model of route-choice behavior with real-time information

Ben-Elia, Eran; Shiftan, Yoram

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Authors

Eran Ben-Elia

Yoram Shiftan



Abstract

This paper presents a learning-based model of route-choice behavior when information is provided in real time. In a laboratory controlled experiment, participants made a long series of binary route-choice trials relying on real-time information and learning from their personal experience reinforced through feedback. A discrete choice model with a Mixed Logit specification, accounting for panel effects, was estimated based on the experiment's data. It was found that information and experience have a combined effect on drivers' route-choice behavior. Informed participants had faster learning rates and tended to base their decisions on memorization relating to previous outcomes whereas non-informed participants were slower in learning, required more exploration and tended to rely mostly on recent outcomes. Informed participants were more prone to risk-seeking and had greater sensitivity to travel time variability. In comparison, non-informed participants appeared to be more risk-averse and less sensitive to variability. These results have important policy implications on the design and implementation of ATIS initiatives. The advantage of incorporating insights from Prospect Theory and reinforced learning to improve the realism of travel behavior models is also discussed. © 2010 Elsevier Ltd. All rights reserved.

Citation

Ben-Elia, E., & Shiftan, Y. (2010). Which road do I take? A learning-based model of route-choice behavior with real-time information. Transportation Research Part A: Policy and Practice, 44(4), 249-264. https://doi.org/10.1016/j.tra.2010.01.007

Journal Article Type Article
Publication Date May 1, 2010
Deposit Date Aug 26, 2010
Publicly Available Date Dec 2, 2016
Journal Transportation Research Part A: Policy and Practice
Print ISSN 0965-8564
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 44
Issue 4
Pages 249-264
DOI https://doi.org/10.1016/j.tra.2010.01.007
Keywords experience, information, panel data, prospect theory, random utility, reinforced learning, route-choice, travel time variability, uncertainty
Public URL https://uwe-repository.worktribe.com/output/979163
Publisher URL http://dx.doi.org/10.1016/j.tra.2010.01.007

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