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MODELING ROUTE CHOICE BEHAVIOR WITH STOCHASTIC LEARNING AUTOMATA.
| 题名： | MODELING ROUTE CHOICE BEHAVIOR WITH STOCHASTIC LEARNING AUTOMATA. |
| 作者： | Ozbay-K; Datta-A; Kachroo-P |
| 关键词： | Equilibrium-Systems; Learning-; Route-choice; Stochastic-learning-automata; Traffic-simulation; Travel-behavior; Travel-time |
| 摘要： | 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. |
| 总页数： | Transportation Research Record. 2001. (1752) pp38-46 (7 Fig., 1 Tab., 9 Ref.) |
| 报告类型： | 科技报告 |
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