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Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching economic, environmental, and societal ramifications.
P. Hunt, D. Robertson, R. Bretherton, and M. C. Royle, “The scoot on-line traffic signal optimisation technique,” Traffic Engineering & Control
1982
Earlier work this paper cites.
P. Lowrie, “Scats-a traffic responsive method of controlling urban traffic,” Sales information brochure published by Roads & Traffic Authority, Sydney, Australia
1990
Earlier work this paper cites.
P. Koonce and L. Rodegerdts, “Traffic signal timing manual.,” tech. rep., United States. Federal Highway Administration, 2008
2008
Earlier work this paper cites.
M. Sweet, “Does traffic congestion slow the economy?,” Journal of Planning Literature
2011
Earlier work this paper cites.
D. Zhao, Y. Dai, and Z. Zhang, “Computational intelligence in urban traffic signal control: A survey,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
2011
Earlier work this paper cites.
F. J. Martinez, C. K. Toh, J.-C. Cano, C. T. Calafate, and P. Manzoni, “A survey and comparative study of simulators for vehicular ad hoc networks (VANETs),” Wireless Communications and Mobile Computing
2011
Earlier work this paper cites.
S.-B. Cools, C. Gershenson, and B. D’Hooghe, “Self-organizing traffic lights: A realistic simulation,” Advances in applied self-organizing systems
2013
Earlier work this paper cites.
P. Varaiya, “Max pressure control of a network of signalized intersections,” Transportation Research Part C: Emerging Technologies
2013
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems
2017
Earlier work this paper cites.
H. Wei, G. Zheng, H. Yao, and Z. Li, “Intellilight: A reinforcement learning approach for intelligent traffic light control,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
2018
Earlier work this paper cites.
P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in 21st international conference on intelligent transportation systems (ITSC)
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
H. Wei, C. Chen, G. Zheng, K. Wu, V. Gayah, K. Xu, and Z. Li, “Presslight: Learning max pressure control to coordinate traffic signals in arterial network,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
2019
Earlier work this paper cites.
T. Chu, J. Wang, L. Codecà, and Z. Li, “Multi-agent deep reinforcement learning for large-scale traffic signal control,” IEEE Transactions on Intelligent Transportation Systems
2019
Earlier work this paper cites.
S. S. S. M. Qadri, M. A. Gökçe, and E. Öner, “State-of-art review of traffic signal control methods: challenges and opportunities,” European transport research review
2020
Cited alongside, same era.
X. Zang, H. Yao, G. Zheng, N. Xu, K. Xu, and Z. Li, “Metalight: Value-based meta-reinforcement learning for traffic signal control,” in Proceedings of the AAAI Conference on Artificial Intelligence
2020
Cited alongside, same era.
C. Chen, H. Wei, N. Xu, G. Zheng, M. Yang, Y. Xiong, K. Xu, and Z. Li, “Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control,” in Proceedings of the AAAI Conference on Artificial Intelligence
2020
Cited alongside, same era.
A. Oroojlooy, M. Nazari, D. Hajinezhad, and J. Silva, “Attendlight: Universal attention-based reinforcement learning model for traffic signal control,” Advances in Neural Information Processing Systems
2020
Cited alongside, same era.
2023
Later among the works it cites.
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al
2023
Later among the works it cites.
Z. Xi, W. Chen, X. Guo, W. He, Y. Ding, B. Hong, M. Zhang, J. Wang, S. Jin, E. Zhou, et al
2023
Later among the works it cites.
2023
Later among the works it cites.
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L. Floridi and M. Chiriatti, “GPT-3: Its nature, scope, limits, and consequences,” Minds and Machines
2020
Cited alongside, same era.
H. Vardhan and J. Sztipanovits, “Rare event failure test case generation in learning-enabled-controllers,” in 6th International Conference on Machine Learning Technologies
2021
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al
2022
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al
2022
Cited alongside, same era.
2022
Cited alongside, same era.
C. Wu, I. Kim, and Z. Ma, “Deep reinforcement learning based traffic signal control: A comparative analysis,” Procedia Computer Science
2023
Cited alongside, same era.
M. Wang, Y. Xu, X. Xiong, Y. Kan, C. Xu, and M.-O. Pun, “ADLight: A universal approach of traffic signal control with augmented data using reinforcement learning,” in Transportation Research Board (TRB) 102nd Annual Meeting
2023
Cited alongside, same era.
S. Bouktif, A. Cheniki, A. Ouni, and H. El-Sayed, “Deep reinforcement learning for traffic signal control with consistent state and reward design approach,” Knowledge-Based Systems
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Pang, M. Wang, Y. Chen, M.-O. Pun, and M. Lepech, “Scalable reinforcement learning framework for traffic signal control under communication delays,” IEEE Open Journal of Vehicular Technology
2024
Closest in time.
H. Gu, S. Wang, X. Ma, D. Jia, G. Mao, E. G. Lim, and C. P. R. Wong, “Large-scale traffic signal control using constrained network partition and adaptive deep reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems
2024
Closest in time.
M. Wang, X. Xiong, Y. Kan, C. Xu, and M.-O. Pun, “UniTSA: A universal reinforcement learning framework for v2x traffic signal control,” IEEE Transactions on Vehicular Technology
2024
Closest in time.
H. Jiang, Z. Li, Z. Li, L. Bai, H. Mao, W. Ketter, and R. Zhao, “A general scenario-agnostic reinforcement learning for traffic signal control,” IEEE Transactions on Intelligent Transportation Systems
2024
Closest in time.
C. Cui, Y. Ma, X. Cao, W. Ye, Y. Zhou, K. Liang, J. Chen, J. Lu, Z. Yang, K.-D. Liao, et al
2024
Closest in time.
D. Fu, X. Li, L. Wen, M. Dou, P. Cai, B. Shi, and Y. Qiao, “Drive like a human: Rethinking autonomous driving with large language models,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
2024
Closest in time.
Y. Tang, X. Dai, and Y. Lv, “Large language model-assisted arterial traffic signal control,” IEEE Journal of Radio Frequency Identification
2024
Closest in time.