Evaluation of Connected and Autonomous Vehicles for Congestion Mitigation: An Approach Based on the Congestion Patterns of Road Networks
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 4
Abstract
Connected and autonomous vehicles (CAVs), with their potential to enhance the interactive perception of vehicle behavior, are expected to benefit traffic congestion and travel efficiency. However, the research scenarios in most current literature are oversimplified and limited, such as a road section or an intersection. To address this issue, this paper proposes a congestion avoidance routing strategy for CAVs to reduce the occurrence and propagation of congestion at the network level. Unlike rerouting after detecting the congestion downstream, the floating-car data are utilized to extract the network congestion patterns, based on which the routes of CAVs are optimized and updated. A simulation framework was built to model the network consisting of CAVs and human-driven vehicles (HDVs). Cooperative adaptive cruise control (CACC) and intelligent driver model (IDM) car-following models were set to characterize the driving behavior of CAVs and HDVs. Simulation experiments were conducted to examine the performance of the proposed routing strategy. The results indicate that the proposed CAV routing strategy can significantly improve the overall congestion state of the network. Compared with the full HDV environment, the vehicles’ average delay can be reduced by up to 46.7% and the travel time by up to 28.2% if all vehicles are switched to CAVs. The sensitivity analysis on CAV penetration rate and vehicle inflow rate shows that the vehicles’ average delay and travel time decreases with the CAV penetration rate increase, and the travel efficiency of CAVs outperforms HDV users sufficiently. Moreover, the benefits of CAVs would be weakened with the increase in vehicle inflow rates. Finally, the findings also provide a reference for CAVs’ centralized control strategy in urban intelligent transportation construction.
Practical Applications
Congestion has always been a problem in traffic networks, and causes substantial economic loss and environmental pollution. However, the emergence of connected and autonomous vehicles brings new potential for improving traffic efficiency via the technologies of autonomous driving, vehicular communication, collaborative perception, and so on. This paper evaluates the impact of CAVs on road congestion under the network mixed with HDVs. A routing control strategy for CAVs based on the road congestion patterns extracted from the taxi trajectory data is proposed, while HDVs are assumed to follow the user equilibrium (UE) principle and choose the fastest path. We obtain that CAVs under the proposed routing strategy demonstrate significant benefits in improving the congestion state of the road network. Regardless of the vehicle inflow rate, the higher the CAV penetration rate, the more significant the mitigation in the average delay and travel time. Also, the increasing CAV penetration rate can promote the reduction of HDVs’ average delay and travel time. Moreover, traveling via CAVs is much more stable and time-saving than HDVs. Finally, with the same model parameters setting and higher vehicle inflow rate, the CAVs’ advantage in mitigating the overall road congestion and improving HDVs’ traveling efficiency will be weakened. These findings provide a reference for CAVs’ centralized control strategy in urban intelligent transportation construction.
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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 research was supported by National Key R&D Program of China under Grant (2020YFC1512004). Zhuo Jiang and Yin Wang contributed equally to this work.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 4 • April 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jun 4, 2023
Accepted: Nov 15, 2023
Published online: Jan 30, 2024
Published in print: Apr 1, 2024
Discussion open until: Jun 30, 2024
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