Evolution of Day-to-Day Route Choice Behaviors under Different Memory-Based Learning Strategies
Owing to the uncertainty of the traffic system and incomplete travel information, travelers usually make route-choice decisions relying on their own experience. In this paper, the authors assume that commuters make their route-choice decisions based on the perceived cost in a logit-based manner, and different memory-based learning strategies on previous travel time, such as smoothed adaptive pattern and peak-end adaptive pattern (anchoring on highest travel time or lowest travel time), are proposed to obtain the perceived cost. A numerical example is also given for comparing the impact of different learning strategies on flow evolution and further illustrating the model. The results show that the peak-end adaptive pattern could capture the commuters’ risk attitude in the route choice process and thus provide a more actual traffic flow, which is obviously helpful to traffic control.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784479896
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Supplemental Notes:
- © 2016 American Society of Civil Engineers.
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Corporate Authors:
American Society of Civil Engineers
1801 Alexander Bell Drive
Reston, VA United States 20191-4400 -
Authors:
- Jiang, Xiao-lan
- Tian, Li-jun
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Conference:
- 16th COTA International Conference of Transportation Professionals
- Location: Shanghai , China
- Date: 2016-6-6 to 2016-6-9
- Publication Date: 2016
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1334-1341
- Monograph Title: CICTP 2016: Green and Multimodal Transportation and Logistics
Subject/Index Terms
- TRT Terms: Commuters; Decision making; Learning; Memory; Route choice; Travel time
- Subject Areas: Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01606945
- Record Type: Publication
- ISBN: 9780784479896
- Files: TRIS, ASCE
- Created Date: Aug 1 2016 9:14AM