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

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

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)205-219
Number of pages15
JournalMathematical Population Studies
Volume21
Issue number4
DOIs
StatePublished - 2 Oct 2014

Keywords

  • experiment
  • real-time information
  • reinforcement learning
  • uncertain network

ASJC Scopus subject areas

  • Demography
  • General Agricultural and Biological Sciences
  • Geography, Planning and Development
  • General Mathematics

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