Perception updating and day-to-day travel choice dynamics in traffic networks with information provision

Mithilesh Jha, Samer Madanat, Srinivas Peeta

Research output: Contribution to journalArticlepeer-review


A Bayesian updating model is developed to capture the mechanism by which travelers update their travel time perceptions from one day to the next in light of information provided by Advanced Traveler Information Systems (ATIS) and their previous experience. The availability and perceived quality of traffic information are explicitly modeled within the proposed framework. The uncertainty associated with a driver's travel time estimate is modeled in a stochastic dynamic framework and is incorporated in a travel choice model. Each driver uses a disutility function of perceived travel time and perceived schedule delay to evaluate the alternative travel choices, then selects an alternative based on the utility maximization principle. The perception updating model and the choice model are integrated with a dynamic traffic simulator (DYNASMART). Empirical results from the simulation experiments and their implications are also presented.

Original languageEnglish (US)
Pages (from-to)189-212
Number of pages24
JournalTransportation Research Part C: Emerging Technologies
Issue number3
StatePublished - Jun 1998


  • ATIS
  • Day-to-day dynamics
  • Driver behaviour
  • Drivers' learning
  • Drivers' perception updating
  • Dynamic network modeling
  • ITS

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications


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