From Search-for-Parking to Dispatch-for-Parking in an Era of Connected and Automated Vehicles: A Macroscopic Approach
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 2
Abstract
The advantage of self-relocation of connected and automated vehicles (CAVs) can eliminate heavy searching-for-parking traffic in areas with limited parking availability. However, the floating trips will exacerbate local traffic congestion and parking competition if relocated CAVs are not well distributed in the network. To address these issues, this paper proposes a centralized dispatching-for-parking system to dynamically dispatch CAVs between different regions to optimize parking resource utilization and traffic distribution. A macroscopic modeling approach is presented with the consideration of mixed traffic flows of human-driven vehicles (HDVs) and CAVs. The system dynamics are modeled with the representation of the macroscopic fundamental diagram (MFD) in a multiregion road network. The objective of the system is to minimize the total network delay, which is formulated by the framework of model predictive control (MPC). Results of the numerical experiments in a two-region network show that the approach improves the performance of system operation and alleviates traffic congestion and imbalance between parking supply and demand in downtown areas. The sensitivity analysis on the level of CAV penetration reveals that the total network delay gradually decreases with the penetration increase, and HDVs benefit more from the MPC controller. The study demonstrates the applicability and implication of the dispatching-for-parking system in an era of CAVs.
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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 work is jointly sponsored by the National Natural Science Foundation of China (52102383), the China Postdoctoral Science Foundation (2021M692428), and the Scientific Research Program of Shanghai Municipal Science and Technology Commission (19DZ1208700; 21DZ1205100). The work of Cong Zhao is supported by the Shanghai Sailing Program (21YF1449400). The authors would like to thank the anonymous reviewers for their constructive comments.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 2 • February 2022
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© 2021 American Society of Civil Engineers.
History
Received: Jul 23, 2021
Accepted: Oct 27, 2021
Published online: Dec 6, 2021
Published in print: Feb 1, 2022
Discussion open until: May 6, 2022
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