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

Eran Ben-Elia, Yoram Shiftan

Research output: Contribution to journalArticlepeer-review

208 Scopus citations


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.

Original languageEnglish
Pages (from-to)249-264
Number of pages16
JournalTransportation Research Part A: Policy and Practice
Issue number4
StatePublished - 1 Jan 2010
Externally publishedYes


  • Experience
  • Information
  • Panel data
  • Prospect Theory
  • Random utility models
  • Reinforced learning
  • Route-choice
  • Travel time variability
  • Uncertainty

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • Management Science and Operations Research


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