Ego-efficient lane changes of connected and automated vehicles with impacts on traffic flow. (May 2022)

• Record Type: Journal Article
• Authors: Wang, Yibing; Wang, Long; Guo, Jingqiu; Papamichail, Ioannis; Papageorgiou, Markos; Wang, Fei-Yue; Bertini, Robert; Hua, Wei; Yang, Qinmin
• Is Part Of: Transportation research. Volume 138(2022)
• Publication Date: 2022-05
• Subjects: Connected and Automated Vehicles -- Ego-efficient Lane Changes -- Traffic Flow Impacts -- Microscopic Simulation -- Reinforcement Learning; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103478
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Background & Significance: connected and automated vehicles (CAVs) with wireless communication and vehicle automation will transform road transport. Impacts of CAVs on traffic flow are still uncertain, but it is vital to understand the impacts as early as possible. Aim & purpose: This paper addresses lane-changing impacts of CAVs on traffic flow of human-driven vehicles and CAVs, and focuses on three important perspective questions. Methods & solutions: Machine learning methods and large-scale microscopic traffic simulation​ were based on for the answers. Results & Conclusions: Interesting and inspiring results were obtained, indicating that ​​CAVs may not simply be a magic cure for the current traffic problems, unless some upper-level coordination may be proposed for CAVs to benefit not only themselves but also the entire traffic flow. Abstract: Connected and automated vehicles (CAVs) enabled by wireless communication and vehicle automation are believed to revolutionize the form and operation of road transport in the next decades. This paper addresses traffic flow effects of CAVs, and focuses on their lane-changing impacts on the mixed traffic flow of CAVs and human-driven vehicles (HVs). At present technical paths towards the development and deployment of CAVs are still uncertain. With CAV technologies getting matured, CAVs are supposed to provide rides of higher efficiency than HVs, beyond improved safety and comfort. In heavy traffic, this would only be achievable via agile and flexible lane changes of CAVs, because longitudinal acceleration would be unhelpful or even impossible in mixed traffic. Such lane changes are expected to be ego-efficient in that they serve solely CAVs' interests without much considering surrounding vehicles, as long as safety constraints are not violated. As road resources are limited, the growth of the CAV population adopting such ego-efficient lane-changing strategies would probably lead to renowned "Tragedy of the Commons". In this context, this paper considers three important prospective questions: A: How to determine an ego-efficient lane-changing strategy for CAVs? B: With increasingly more CAVs introduced each adopting the ego-efficient lane-changing strategy, what is the impact on traffic flow? C: How to determine a system-efficient lane-changing strategy for CAVs? These forward-looking issues are addressed from the perspectives of microscopic traffic simulation and reinforcement learning. Without any constraint on the lane-changing incentive, the developed lane-changing strategy was found to be beneficial for CAVs and the entire traffic flow only if the market penetration rate (MPR) of CAVs is less than 50%. With an appropriate constraint placed, however, the lane-changing strategy was found to become consistently beneficial for the entire traffic flow at any MPR. These findings suggest that CAVs may not simply be a magic cure for traffic problems that the society is currently facing, unless some upper-level coordination may be proposed for CAVs to benefit not only themselves but also the entire traffic. This is also consistent with what "Tragedy of the Commons" suggests.