Personalized Modeling of Travel Behaviors and Traffic Dynamics
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 10
Abstract
Emerging mobile Internet applications have become valuable data sources for fine-grained transportation analysis, which allows the introduction of the concept of Personalization in both microscopic and macroscopic modeling of travel behaviors and traffic dynamics. Inspired by personalized recommendation systems, the personalized transportation models emphasize the importance of individual and local information. Two representative cases are presented in this study and two architectures, namely the travel behavior modeling architecture and the geoinformation modeling architecture, are proposed to address the problems of bike-sharing destination prediction and ensemble of ride-hailing demand predictors, respectively. Their performance has been verified by two case studies using the Mobike bike-sharing data and the DiDi ride-hailing demand data.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement No. 101025896.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 10 • October 2022
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© 2022 American Society of Civil Engineers.
History
Received: Dec 6, 2021
Accepted: Jun 6, 2022
Published online: Aug 8, 2022
Published in print: Oct 1, 2022
Discussion open until: Jan 8, 2023
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