Modeling Route Choice Behavior with Stochastic Learning Automata
Document Type
Conference Proceeding
Publication Date
2001
Publisher
Transportation Research Board
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 models (Stochastic processes); Traffic engineering; Traffic flow; Travel time (Traffic engineering)
Disciplines
Applied Mathematics | Controls and Control Theory | Theory and Algorithms | Transportation
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
Repository Citation
Ozbay, K.,
Datta, A.,
Kachroo, P.
(2001).
Modeling Route Choice Behavior with Stochastic Learning Automata.
38-46.
Transportation Research Board.
https://digitalscholarship.unlv.edu/ece_fac_articles/91
