00818735

Component

https://doi.org/10.3141/1752-06

http://scholar.google.com...hroo&publication_year=2001

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.

This paper appears in Transportation Research Record No. 1752, Travel Patterns and Behavior; Effects of Communications Technology.

English

p. 38-46

2001

Transportation Research Record

0309072131

Figures (7) ; References (9) ; Tables (1)

Equilibrium (Systems); Learning; Route choice; Traffic simulation; Travel behavior; Travel time

Stochastic learning automata

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Oct 1 2001 12:00AM

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