An Attention Reinforcement Learning–Based Strategy for Large-Scale Adaptive Traffic Signal Control System-Reference-Cited by-同舟云学术

An Attention Reinforcement Learning–Based Strategy for Large-Scale Adaptive Traffic Signal Control System

Published:2024-03 Issue:3 Volume:150 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Han Gengyue¹^ORCID,Liu Xiaohan²,Wang Hao³^ORCID,Dong Changyin⁴,Han Yu⁴

Affiliation:

1. Graduate Student, School of Transportation, Southeast Univ., Nanjing 211189, PR China; Jiangsu Key Laboratory of Urban ITS, Southeast Univ., Nanjing 210096, PR China; Graduate Student, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast Univ., Nanjing 210096, PR China. ORCID: .

2. Graduate Student, School of Transportation, Southeast Univ., Nanjing 211189, PR China; Jiangsu Key Laboratory of Urban ITS, Southeast Univ., Nanjing 210096, PR China; Graduate Student, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast Univ., Nanjing 210096, PR China; Hikvision Digital Technology Company Limited, No.555 Qianmo Rd., Binjiang District, Hangzhou 310051, PR China.

3. Professor, School of Transportation, Southeast Univ., Nanjing 211189, PR China; Professor, Jiangsu Key Laboratory of Urban ITS, Southeast Univ., Nanjing 210096, PR China; Professor, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast Univ., Nanjing 210096, PR China (corresponding author). ORCID: .

4. Professor, School of Transportation, Southeast Univ., Nanjing 211189, PR China; Professor, Jiangsu Key Laboratory of Urban ITS, Southeast Univ., Nanjing 210096, PR China; Professor, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast Univ., Nanjing 210096, PR China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-8261

Reference42 articles.

1. Reinforcement Learning for True Adaptive Traffic Signal Control

2. Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events

3. A novel reinforcement learning-based cooperative traffic signal system through max-pressure control;Boukerche A.;IEEE Trans. Veh. Technol.,2021

4. Choe C. J. S. Baek B. Woon and S. H. Kong. 2019. “Deep Q learning with LSTM for traffic light control.” In Proc. 2018 24th Asia-Pacific Conf. on Communications APCC 2018 331–336. New York: IEEE.

5. Multi-agent deep reinforcement learning for large-scale traffic signal control;Chu T.;IEEE Trans. Intell. Transp. Syst.,2020

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