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.


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    Title :

    Modeling Route Choice Behavior with Stochastic Learning Automata


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2001-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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