Route Choice in an Uncertain Environment: Algorithms and Behavioral Studies
Event details
| Date | 28.05.2014 |
| Hour | 11:00 › 12:00 |
| Speaker | Prof. Song Gao |
| Location | |
| Category | Conferences - Seminars |
Transportation systems are inherently uncertain due to disruptions such as bad weather and incident, and the randomness of traveler' choices. Real-time information allows travelers to adapt to actual traffic conditions and potentially mitigate the adverse effect of uncertainty. Both algorithmic and behavioral studies of adaptive routing are presented. A series of optimal adaptive routing problems are investigated, where time-dependent travel times are modeled as correlated random variables and various assumptions on the real-time information accessibility are made. Behavioral studies of adaptive route choice in both one-shot and day-to-day learning contexts based on stated preferences data show that travelers can plan ahead for traffic information not yet available. Two modeling paradigms for route choice under unreliable travel times, utility maximization based on the prospect theory and non-compensatory heuristic, are compared. The non-compensatory heuristic is found to be potentially a suitable alternative to the conventional utility maximization approach. The ongoing work of developing a history-dependent route choice learning model for realistic networks is also discussed.
Practical information
- Expert
- Free
- This event is internal
Organizer
- Transport and Mobility Laboratory
Contact
- Prof. Michel Bierlaire