Modeling route choice behavior with stochastic learning automata

Kaan Ozbay, Aleek Datta, Pushkin Kachroo

Research output: Contribution to journalArticlepeer-review


Day-to-day route choice behavior of drivers is analyzed by the introduction of a new route choice model developed using stochastic learning automata (SLA) theory. This day-to-day route choice model addresses the learning behavior of travelers on the basis of experienced travel time and day-to-day learning. To calibrate the penalties of the model, an Internet-based route choice simulator (IRCS) was developed. The IRCS is a traffic simulation model that represents within-day and day-to-day fluctuations in traffic and was developed using Java programming. The calibrated SLA model is then applied to a simple transportation network to test if global user equilibrium, instantaneous equilibrium, and driver learning have occurred over a period of time. It is observed that the developed stochastic learning model accurately depicts the day-to-day learning behavior of travelers. Finally, the sample network converges to equilibrium in terms of both global user and instantaneous equilibrium.

Original languageEnglish (US)
Pages (from-to)38-46
Number of pages9
JournalTransportation Research Record
Issue number1752
StatePublished - 2001

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


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