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Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem.
A survey on traffic signal control methods
Wei, H., Zheng, G., Gayah, V., and Li, Z · 1904
Earlier work this paper cites.
Colight: Learning network-level cooperation for traffic signal control
Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., and Li, Z · 1922
Earlier work this paper cites.
The scoot on-line traffic signal optimisation technique
Hunt, P., Robertson, D., Bretherton, R., and Royle, M. C · 1982
Earlier work this paper cites.
Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks
Tassiulas, L. and Ephremides, A · 1990
Earlier work this paper cites.
Scats: A traffic responsive method of controlling urban traffic control
Lowrie, P · 1992
Earlier work this paper cites.
Self-organizing traffic lights
Gershenson, C · 2004
Earlier work this paper cites.
Traffic signal timing manual
Koonce, P. and Rodegerdts, L · 2008
Earlier work this paper cites.
Self-organizing urban transportation systems
Gershenson, C · 2012
Earlier work this paper cites.
Distributed traffic signal control for maximum network throughput
Wongpiromsarn, T., Uthaicharoenpong, T., Wang, Y., Frazzoli, E., and Wang, D · 2012
Cited alongside, same era.
Self-organizing traffic lights: A realistic simulation
Cools, S.-B., Gershenson, C., and D’Hooghe, B · 2013
Cited alongside, same era.
Max pressure control of a network of signalized intersections
Varaiya, P · 2013
Cited alongside, same era.
Maximum pressure controller for stabilizing queues in signalized arterial networks
Kouvelas, A., Lioris, J., Fayazi, S. A., and Varaiya, P · 2014
Cited alongside, same era.
Decentralized signal control for urban road networks
Le, T., Kovács, P., Walton, N., Vu, H. L., Andrew, L. L., and Hoogendoorn, S. S · 2015
Cited alongside, same era.
Coordinated deep reinforcement learners for traffic light control
Van der Pol, E. and Oliehoek, F. A · 2016
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
Later among the works it cites.
Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario
Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., and Li, Z · 2019
Later among the works it cites.
Learning phase competition for traffic signal control
Zheng, G., Xiong, Y., Zang, X., Feng, J., Wei, H., Zhang, H., Li, Y., Xu, K., and Li, Z · 2019
Later among the works it cites.
Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control
Chen, C., Wei, H., Xu, N., Zheng, G., Yang, M., Xiong, Y., Xu, K., and Li, Z · 2020
Later among the works it cites.
Max-pressure signal control with cyclical phase structure
Levin, M. W., Hu, J., and Odell, M · 2020
Later among the works it cites.
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Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Back-pressure traffic signal control with unknown routing rates
Gregoire, J., Frazzoli, E., de La Fortelle, A., and Wongpiromsarn, T
Cited in the paper.
Capacity-aware backpressure traffic signal control
Gregoire, J., Qian, X., Frazzoli, E., De La Fortelle, A., and Wongpiromsarn, T
Cited in the paper.
Presslight: Learning max pressure control to coordinate traffic signals in arterial network
Wei, H., Chen, C., Zheng, G., Wu, K., Gayah, V., Xu, K., and Li, Z
Cited in the paper.
The bounds of improvements toward real-time forecast of multi-scenario train delays
Wu, J., Wang, Y., Du, B., Wu, Q., Zhai, Y., Shen, J., Zhou, L., Cai, C., Wei, W., and Zhou, Q
Cited in the paper.
Efficient pressure: Improving efficiency for signalized intersections, 2021b
Wu, Q., Zhang, L., Shen, J., Lü, L., Du, B., and Wu, J
Cited in the paper.
Attendlight: Universal attention-based reinforcement learning model for traffic signal control
Oroojlooy, A., Nazari, M., Hajinezhad, D., and Silva, J · 2020
Later among the works it cites.
PRGLight: A novel traffic light control framework with pressure-based-reinforcement learning and graph neural network
Zhao, C., Hu, X., and Wang, G · 2021
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