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

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...

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Bibliographic Details
Published in:Transportation research. Part A, Policy and practice Policy and practice, 2010-05, Vol.44 (4), p.249-264
Main Authors: Ben-Elia, Eran, Shiftan, Yoram
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Experience
Experience Information Panel data Prospect Theory Random utility models Reinforced learning Route-choice Travel time variability Uncertainty
Ground, air and sea transportation, marine construction
Information
Panel data
Prospect Theory
Random utility models
Reinforced learning
Route-choice
Travel time variability
Uncertainty
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Summary: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.
ISSN:0965-8564
1879-2375
DOI:10.1016/j.tra.2010.01.007