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Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns.
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2018
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Z. Wang, H. Zhu, M. He, Y. Zhou, X. Luo, and N. Zhang, “Gan and multi-agent drl based decentralized traffic light signal control,” IEEE Transactions on Vehicular Technology , vol. 71, no. 2, pp. 1333–1348, 2021
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H. Su, Y. D. Zhong, B. Dey, and A. Chakraborty, “Emvlight: A decentralized reinforcement learning framework for efficient passage of emergency vehicles,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 4, 2022, pp. 4593–4601
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2018
Cited alongside, same era.
M. Aslani, S. Seipel, M. S. Mesgari, and M. Wiering, “Traffic signal optimization through discrete and continuous reinforcement learning with robustness analysis in downtown tehran,” Advanced Engineering Informatics , vol. 38, pp. 639–655, 2018
2018
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P. Alvarez 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 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . Maui, USA: IEEE, Nov. 2018, pp. 2575–2582
2018
Cited alongside, same era.
2019
Cited alongside, same era.
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 , vol. 21, no. 3, pp. 1086–1095, 2019
2019
Cited alongside, same era.
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, pp. 1290–1298
2019
Cited alongside, same era.
X. Liang, X. Du, G. Wang, and Z. Han, “A deep reinforcement learning network for traffic light cycle control,” IEEE Transactions on Vehicular Technology , vol. 68, no. 2, pp. 1243–1253, 2019
2019
Cited alongside, same era.
C.-H. Du, Y.-S. Chiang, K.-C. Tsai, L.-C. Liu, M.-F. Tsai, and C.-J. Wang, “Fridays: A financial risk information detecting and analyzing system,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 9853–9854
2019
Cited alongside, same era.
2022
Later among the works it cites.
M. Noaeen, A. Naik, L. Goodman, J. Crebo, T. Abrar, Z. S. H. Abad, A. L. Bazzan, and B. Far, “Reinforcement learning in urban network traffic signal control: A systematic literature review,” Expert Systems with Applications , p. 116830, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “Ai in health and medicine,” Nature Medicine , vol. 28, no. 1, pp. 31–38, 2022
2022
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M. Cao, V. O. Li, and Q. Shuai, “Book your green wave: Exploiting navigation information for intelligent traffic signal control,” IEEE Transactions on Vehicular Technology , vol. 71, no. 8, pp. 8225–8236, 2022
2022
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S. M. A. Shabestary and B. Abdulhai, “Adaptive traffic signal control with deep reinforcement learning and high dimensional sensory inputs: Case study and comprehensive sensitivity analyses,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 11, pp. 20 021–20 035, 2022
2022
Later among the works it cites.
S. Ghanadbashi and F. Golpayegani, “Using ontology to guide reinforcement learning agents in unseen situations,” Applied Intelligence , vol. 52, no. 2, pp. 1808–1824, 2022
2022
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A. Paul and S. Mitra, “Exploring reward efficacy in traffic management using deep reinforcement learning in intelligent transportation system,” ETRI Journal , vol. 44, no. 2, pp. 194–207, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
M. Bouadi, B. Jia, R. Jiang, X. Li, and Z.-Y. Gao, “Stability analysis of stochastic second-order macroscopic continuum models and numerical simulations,” Transportation Research Part B: Methodological , vol. 164, pp. 193–209, 2022
2022
Later among the works it cites.
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 Proceedings of 103th Transportation Research Board Annual Meeting (TRB 2023) , 2023
2023
Later among the works it cites.
M. Yuan, M.-O. Pun, and D. Wang, “Rényi state entropy maximization for exploration acceleration in reinforcement learning,” IEEE Transactions on Artificial Intelligence , vol. 4, no. 5, pp. 1154–1164, 2023
2023
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T. M. Moerland, J. Broekens, A. Plaat, C. M. Jonker et al. , “Model-based reinforcement learning: A survey,” Foundations and Trends® in Machine Learning , vol. 16, no. 1, pp. 1–118, 2023
2023
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C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 7464–7475
2023
Later among the works it cites.
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 , pp. 1–16, 2024
2024
Closest in time.
Y. Gu, K. Zhang, Q. Liu, W. Gao, L. Li, and J. Zhou, “ π \pi -light: Programmatic interpretable reinforcement learning for resource-limited traffic signal control,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 19, 2024, pp. 21 107–21 115
2024
Closest in time.