CVLight: Decentralized learning for adaptive traffic signal control with connected vehicles
This paper develops a decentralized reinforcement learning (RL) scheme for multi-intersection adaptive traffic signal control (TSC), called “CVLight”, that leverages data collected from connected vehicles (CVs). The state and reward design facilitates coordination among agents and considers travel delays collected by CVs. A novel algorithm, Asymmetric Advantage Actor-critic (Asym-A2C), is proposed where both CV and non-CV information is used to train the critic network, while only CV information is used to execute optimal signal timing. Comprehensive experiments show the superiority of CVLight over state-of-the-art algorithms under a 2-by-2 synthetic road network with various traffic demand patterns and penetration rates. The learned policy is then visualized to further demonstrate the advantage of Asym-A2C. A pre-train technique is applied to improve the scalability of CVLight, which significantly shortens the training time and shows the advantage in performance under a 5-by-5 road network. A case study is performed on a 2-by-2 road network located in State College, Pennsylvania, USA, to further demonstrate the effectiveness of the proposed algorithm under real-world scenarios. Compared to other baseline models, the trained CVLight agent can efficiently control multiple intersections solely based on CV data and achieve the best performance, especially under low CV penetration rates.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Mo, Zhaobin
- Li, Wangzhi
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0000-0003-0682-4097
- Fu, Yongjie
- Ruan, Kangrui
- Di, Xuan
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0000-0003-2925-7697
- Publication Date: 2022-8
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: 103728
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Serial:
- Transportation Research Part C: Emerging Technologies
- Volume: 141
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0968-090X
- Serial URL: http://www.sciencedirect.com/science/journal/0968090X
Subject/Index Terms
- TRT Terms: Adaptive control; Connected vehicles; Machine learning; Traffic signal control systems
- Geographic Terms: State College (Pennsylvania)
- Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01849063
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 22 2022 9:35AM