Modeling Route Choice Behavior with Stochastic Learning Automata

Document Type

Article

Publication Date

2001

Publication Title

Journal of the Transportation Research Board

Issue

1752

First page number:

38

Last page number:

46

Abstract

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.

Keywords

Automobile drivers; Learning; Psychology of; Motor vehicle drivers; Traffic engineering

Disciplines

Applied Mathematics | Civil and Environmental Engineering | Controls and Control Theory | Education | Social and Behavioral Sciences | Urban Studies and Planning

Language

English

Permissions

Use Find in Your Library, contact the author, or use interlibrary loan to garner a copy of the article. Publisher copyright policy allows author to archive post-print (author’s final manuscript). When post-print is available or publisher policy changes, the article will be deposited

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