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.
Modeling Route Choice Behavior with Stochastic Learning Automata
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 1752 , 1 ; 38-46
2001-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Modeling Route Choice Behavior with Stochastic Learning Automata
Online Contents | 2001
|Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior
Online Contents | 2002
|Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior
Transportation Research Record | 2002
|Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior
British Library Conference Proceedings | 2002
|Modeling Route Choice Behavior
Transportation Research Record | 2007
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