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Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network, which in turn enhances fuel utilization efficiency, air quality, and road safety, benefiting society as a whole.
Scoot-a traffic responsive method of coordinating signals
PB Hunt, DI Robertson, RD Bretherton, and RI Winton · 1981
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Maxband: A versatile program for setting signals on arteries and triangular networks
John DC Little, Mark D Kelson, and Nathan H Gartner · 1981
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Scats-application and field comparison with a transyt optimised fixed time system
JY Luk, AG Sims, and PR Lowrie · 1982
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Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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A real-time traffic signal control system: architecture, algorithms, and analysis
Pitu Mirchandani and Larry Head · 2001
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Rethinking traffic congestion
Brian D Taylor · 2002
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Traffic engineering
Roger P Roess, Elena S Prassas, and William R McShane · 2004
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Traffic signal timing manual
Peter Koonce and Lee Rodegerdts · 2008
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Reinforcement learning with function approximation for traffic signal control
LA Prashanth and Shalabh Bhatnagar · 2010
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Self-organizing traffic lights: A realistic simulation
Seung-Bae Cools, Carlos Gershenson, and Bart D’Hooghe · 2013
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Max pressure control of a network of signalized intersections
Pravin Varaiya · 2013
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Air pollution and health risks due to vehicle traffic
Kai Zhang and Stuart Batterman · 2013
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2016
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Coordinated deep reinforcement learners for traffic light control
Elise Van der Pol and Frans A Oliehoek · 2016
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Optimized structure of the traffic flow forecasting model with a deep learning approach
Hao-Fan Yang, Tharam S Dillon, and Yi-Ping Phoebe Chen · 2016
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
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Hierarchically learned view-invariant representations for cross-view action recognition
Yang Liu, Zhaoyang Lu, Jing Li, and Tao Yang · 2018
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Global temporal representation based cnns for infrared action recognition
Yang Liu, Zhaoyang Lu, Jing Li, Tao Yang, and Chao Yao · 2018
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Large-scale traffic signal control using a novel multiagent reinforcement learning
Xiaoqiang Wang, Liangjun Ke, Zhimin Qiao, and Xinghua Chai · 2020
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Metalight: Value-based meta-reinforcement learning for traffic signal control
Xinshi Zang, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, and Zhenhui Li · 2020
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Designing reinforcement learning agents for traffic signal control with the right goals: a time-loss based approach
Marcelo D’Almeida, Aline Paes, and Daniel Mossé · 2021
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Urban traffic light control via active multi-agent communication and supply-demand modeling
Xin Guo, Zhengxu Yu, Pengfei Wang, Zhongming Jin, Jianqiang Huang, Deng Cai, Xiaofei He, and Xiansheng Hua · 2021
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Network-scale traffic signal control via multiagent reinforcement learning with deep spatiotemporal attentive network
Hao Huang, Zhiqun Hu, Zhaoming Lu, and Xiangming Wen · 2021
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Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Intellilight: A reinforcement learning approach for intelligent traffic light control
Hua Wei, Guanjie Zheng, Huaxiu Yao, and Zhenhui Li · 2018
Cited alongside, same era.
Multi-agent deep reinforcement learning for large-scale traffic signal control
Tianshu Chu, Jie Wang, Lara Codecà, and Zhaojian Li · 2019
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Deep image-to-video adaptation and fusion networks for action recognition
Yang Liu, Zhaoyang Lu, Jing Li, Tao Yang, and Chao Yao · 2019
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QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
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Cooperative deep reinforcement learning for large-scale traffic grid signal control
Tian Tan, Feng Bao, Yue Deng, Alex Jin, Qionghai Dai, and Jie Wang · 2019
Cited alongside, same era.
Presslight: Learning max pressure control to coordinate traffic signals in arterial network
Hua Wei, Chacha Chen, Guanjie Zheng, Kan Wu, Vikash Gayah, Kai Xu, and Zhenhui Li · 2019
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An integrated reinforcement learning and centralized programming approach for online taxi dispatching
Enming Liang, Kexin Wen, William HK Lam, Agachai Sumalee, and Renxin Zhong · 2021
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Semantics-aware adaptive knowledge distillation for sensor-to-vision action recognition
Yang Liu, Keze Wang, Guanbin Li, and Liang Lin · 2021
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Tianshou: A highly modularized deep reinforcement learning library
Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Hang Su, and Jun Zhu · 2021
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Efficient pressure: Improving efficiency for signalized intersections
Qiang Wu, Liang Zhang, Jun Shen, Linyuan Lü, Bo Du, and Jianqing Wu · 2021
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Hierarchically and cooperatively learning traffic signal control
Bingyu Xu, Yaowei Wang, Zhaozhi Wang, Huizhu Jia, and Zongqing Lu · 2021
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Graphlight: Graph-based reinforcement learning for traffic signal control
Zheng Zeng · 2021
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Expression is enough: Improving traffic signal control with advanced traffic state representation
Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Jianqing Wu, and Bo Du · 2021
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Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting
Changlu Chen, Yanbin Liu, Ling Chen, and Chengqi Zhang · 2022
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Traffic signal control with adaptive online-learning scheme using multiple-model neural networks
Wanshi Hong, Gang Tao, Hong Wang, and Chieh Wang · 2022
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Tcgl: Temporal contrastive graph for self-supervised video representation learning
Yang Liu, Keze Wang, Lingbo Liu, Haoyuan Lan, and Liang Lin · 2022
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Causal reasoning meets visual representation learning: A prospective study
Yang Liu, Yu-Shen Wei, Hong Yan, Guan-Bin Li, and Liang Lin · 2022
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Synchronous spatiotemporal graph transformer: A new framework for traffic data prediction
Tian Wang, Jiahui Chen, Jinhu Lü, Kexin Liu, Aichun Zhu, Hichem Snoussi, and Baochang Zhang · 2022
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Hybrid-order representation learning for electricity theft detection
Yuying Zhu, Yang Zhang, Lingbo Liu, Yang Liu, Guanbin Li, Mingzhi Mao, and Liang Lin · 2022
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Cross-modal causal relational reasoning for event-level visual question answering
Yang Liu, Guanbin Li, and Liang Lin · 2023
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Urban regional function guided traffic flow prediction
Kuo Wang, LingBo Liu, Yang Liu, GuanBin Li, Fan Zhou, and Liang Lin · 2023
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