A Developed generalized mathematical traffic flow model for a single intersection

Open Access

Issue		E3S Web Conf. Volume 729, 2026 1^st Sustainable Power, Energy, Transportation, and Materials Conference (SPETM 2026)


Article Number		06003
Number of page(s)		8
Section		Smart Grid and Smart City Technologies
DOI		https://doi.org/10.1051/e3sconf/202672906003
Published online		31 July 2026

C. Cai, C. K. Wong, and B. G. Heydecker, Adaptive traffic signal control using approximate dynamic programming. Transportation Research Part C: Emerging Technologies, 17, 5, 456–474 (2009). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
H. Wei, G. Zheng, H. Yao, and Z. Li, IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), pp. 2496–2505 (2018). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
F. Al-Turjman, & A. Malekloo. Smart parking in IoT-enabled cities. Sustainable Cities and Society, 49, 101608 (2019). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
C.-H. Wan and M.-C. Hwang, Value-based deep reinforcement learning for adaptive isolated intersection signal control. IET Intelligent Transport Systems, 12, 9, 1005–1010 (2018). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
H. Wei, C. Chen, G. Zheng, K. Wu, V. Gayah, K. Xu, & Z. Li, PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network, KDD '19: Proceedings of the 25^th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, p. 1290–1298 (2019). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
M. Noaeen, A. Naik, L. Goodman, J. Crebo, M. Abrar, M. S. Hossain, M. R. Islam, & M.A.M. Hasan, Reinforcement learning in urban network traffic signal control: A systematic literature review, Expert Systems with Applications, 199, 116830 (2022). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11, 1, 189, (2019). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
B. R. Kiran, I. Sobh, V. Talpaert, P. Mannion, A. A. Al Sallab, S. Yogamani, and P. Pérez, Deep Reinforcement Learning for Autonomous Driving: A Survey. IEEE Transactions on Intelligent Transportation Systems, 23, 6, 4909–4926 (2022). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
T. Yuan, W. Da Rocha Neto, C.E. Rothenberg, K. Obraczka, C. Barakat, & T. Turletti. Machine learning for next-generation intelligent transportation systems: A survey. Transactions on Emerging Telecommunications Technologies, 33(4), e4427 (2022). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
M. Aslani, M. S. Mesgari, and M. A. Wiering, Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events. Transportation Research Part C: Emerging Technologies, 85, 732–752 (2017). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
L. Ma, Y. Liu, Y. Liu, C. Ma, S. Wang, Coordinated multi-intersection traffic signal control using a policy-regulated deep Q-network. Sustainability, 18, 3, 1510 (2026). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
R. Wang, J. Zhang, X. Wang, H. He, Y. Zou, Multi-modal and multi-agent reinforcement learning framework for urban traffic flow prediction and signal control optimization. Scientific Reports, 16, 7612 (2026). DOI: https://doi.org/10.1115/L2822013 [Google Scholar]
H. Xiao, H. Li, Z. Wang, Z. Yang, S. Qi, J. Zhang, D. Cai, J. Yin, Intelligent traffic signal control based on reinforcement learning: A survey. Artificial Intelligence Review, 59, 128 (2026). https://doi.org/10.1115/L2822013 [Google Scholar]

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