[1] ZHENG F, VAN-ZUYLEN H J, LIU X, et al. Reliability
based traffic signal control for urban arterial roads[J].
IEEE Transactions on Intelligent Transportation
Systems, 2017, 18(3): 643-655.
[2]ABDULHAI B, PRINGLE R, KARAKOULAS G J.
Reinforcement learning for true adaptive traffic signal
control[J]. Journal of Transportation Engineering, 2003,
129(3): 278-285.
[3] GENDERS W, RAZAVI S. Using a deep reinforcement
learning agent for traffic signal control[J]. ArXiv Preprint
ArXiv: 1611.01142, 2016.
[4]
WEI H, CHEN C, ZHENG G, et al. Presslight: Learning
max pressure control to coordinate traffic signals in
arterial network[C]. Anchorage: Proceedings of the 25th
ACM Sigkdd International Conference on Knowledge
Discovery & Data Mining, 2019.
[5] ZHENG G, XIONG Y, ZANG X, et al. Learning phase
competition for traffic signal control[C]. BeiJing:
Proceedings of the 28th ACM International Conference
on Information and Knowledge Management, 2019.
[6] CHEN C, WEI H, XU N, et al. Toward a thousand lights:
Decentralized deep reinforcement learning for large
scale traffic signal control[C]. New York: Proceedings of
the AAAI Conference on Artificial Intelligence, 2020.
[7]张玺君,聂生元,李喆,等.基于自注意力机制的深度强化学习交通信号控制[J].交通运输系统工程与信息, 2024, 24(2): 96-104. [ZHANG X J, NIE S Y, LI Z,
et al. Traffic signal control with deep reinforcement
learning and self-attention mechanism[J]. Journal of
Transportation Systems Engineering and Information
Technology, 2024, 24(2): 96-104.]
[8] BALAJI P G, GERMAN X, SRINIVASAN D. Urban
traffic signal control using reinforcement learning agents
[J]. IET Intelligent Transport Systems, 2010, 4(3): 177
188.
[9]
KUYER L, WHITESON S, BAKKER B, et al. Multiagent
reinforcement learning for urban traffic control using
coordination
graphs[C].
Berlin:
Joint
European
Conference on Machine Learning and Knowledge
Discovery in Databases, Springer, 2008.
[10] POL V, OLIEHOEK F A. Coordinated deep
reinforcement learners for traffic light control[C].
Barcelona: Proceedings of Learning, Inference and
Control of Multi-agent Systems, 2016.
[11] 舒凌洲,吴佳,王晨.基于深度强化学习的城市交通信号控制算法[J]. 计算机应用,2019, 39(5): 1495-1499.
[SHU L Z, WU J, WANG C. Urban traffic signal control
based on deep reinforcement learning[J]. Journal of
Computer Applications, 2019, 39(5): 1495-1499.]
[12] ZHU F, AZIZ H M A, QIAN X, et al. A Junction-tree
based learning algorithm to optimize network wide
traffic control: A coordinated multi-agent framework[J].
Transportation Research Part C: Emerging Technologies,
2015, 58(PC): 487-501.
[13] 陈喜群, 朱奕璋,谢宁珂,等.基于异构多智能体自注意力网络的路网信号协调顺序优化方法[J].交通运输系统工程与信息,2024,24(3): 114-126. [CHEN X Q,
ZHU Y Z, XIE N K, et al. Coordinated sequential
optimization for network-wide traffic signal control based
on heterogeneous multi-agent transformer[J]. Journal of
Transportation Systems Engineering and Information
Technology, 2024, 24(3): 114-126.]
[14] WEI H, XU N, ZHANG H, et al. Colight: Learning
network-level cooperation for traffic signal control[C].
Beijing: Proceedings of the 28th ACM International
Conference on Information and Knowledge Management,
2019.
[15] NISHI T, OTAKI K, HAYAKAWA K, et al. Traffic signal
control based on reinforcement learning with graph
convolutional neural nets[C]. Hawaii: 21st International
Conference on Intelligent Transportation Systems (ITSC),
IEEE, 2018.
[16] LIORIS J, KURZHANSKIY A, VARAIYA P. Adaptive
max pressure control of network of signalized
intersections[C]. Florianopolis: International Federation
of Automatic Control (IFAC), 2016.
[17] XU B, WANG Y, WANG Z, et al. Hierarchically and
cooperatively learning traffic signal control[C]. Beijing:
Proceedings of the AAAI Conference on Artificial
Intelligence, 2021.
[18] WEI H, ZHENG G, YAO H, et al. Intellilight: A
reinforcement learning approach for intelligent traffic
light control[C]. London: Proceedings of the 24th ACM
SIGKDD International Conference on Knowledge
Discovery & Data Mining, 2018.
[19] 闫超, 相晓嘉,徐昕,等.多智能体深度强化学习及其可扩展性与可迁移性研究综述[J].控制与决策,2022,
37(12): 3083-3102. [YAN C, XIANG X J, XU X, et al. A
survey on scalability and transferability of multi-agent
deep reinforcement learning[J]. Control and Decision,
2022, 37(12): 3083-3102.]
[20] 朱仁伟,吴迪,羊钊.信号交叉口排队溢流控制触发条件及方案设计[J].武汉理工大学学报(交通科学与工程版), 2018, 42(6): 971-976. [ZHU R W, WU D, YANG
Z. Triggering conditions and scheme design of queuing
overflow control at signalized intersections[J]. Journal of
Wuhan University of Technology (Transportation Science
&. Engineering), 2018, 42(6): 971-976.]
[21] XU H, LI K, ZHENG M. An online queue length
estimation algorithm for adaptive for real-time prediction
of delay and maximum queue length at signalized
intersections[J]. Journal of the Transportation Research
Board, 2007, 203(5): 69-80
[22] MEI H, LEI X, DA L, et al. Libsignal: An open library for
traffic signal control[J]. Machine Learning, 2024, 113(8):
5235-5271.
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