Modeling Routing Behavior Learning Process for Vacant Taxis in a Congested Urban Traffic Network
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 6
Abstract
In this paper, we present a modeling framework and approach to capture vacant taxi drivers’ route choice behavior learning process and simulate their changes of routing decisions over time due to updated experiences of the traffic and passenger’s information. Efforts to unveil their behavioral learning process were rather limited, although some researchers focused on the modeling of routing behavior. We focused on the street-hailing of vacant taxi drivers, who selected a route to minimize the search time for picking-up a waiting customer along the road, which was determined by the traffic information and customer arrival rate. At the end of each learning cycle, or “learning day,” taxi drivers updated their knowledge on the traffic and passengers based on their newly gained experience, and made corresponding changes to their route choice at the next learning day until an optimal route had been found. Both analytical and numerical analysis were conducted on the Taipei traffic simulation network. The case study results showed that the proposed model was able to reasonably capture taxi drivers’ changes of route choice.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request, including the traffic network, O-D demand table, taxi passenger demand data, link travel time, and link volume.
Acknowledgments
The research was sponsored by the Key Research and Development Program of China (No. 2018YFB1600900) and Zhejiang province public welfare scientific research project (Grant No.LGF18E080003).
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 6 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Feb 16, 2019
Accepted: Nov 5, 2019
Published online: Mar 31, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 31, 2020
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