AI-based dynamic optimization of smart traffic signals for improved mobility efficiency
Jiangeng Long
Faculty of Information Technology, City University Malaysia/Jiangxi Vocational & Technical College of Information Application
M. KazemChamran
Faculty of Information Technology, City University Malaysia
DOI: https://doi.org/10.59429/esta.v13i1.13402
Keywords: artificial intelligence; intelligent transportation systems; adaptive signal control; reinforcement learning; multi-agent control; traffic efficiency
Abstract
Artificial intelligence (AI) can improve urban mobility by adapting traffic signals to changing demand, incidents, and multimodal priorities. We propose a fieldable framework combining real-time state estimation, short-horizon prediction, and constraint-aware reinforcement learning. The policy suggests phase, split, and offset updates, while a safety and fairness layer enforces minimum green, clearance, pedestrian service, and side-street protection. Neighborhood messaging enables corridor coordination, and deployment uses monitoring and rollback. Illustrative studies show reduced delay and queues with higher throughput.
References
[1] P. Michailidis, I. Michailidis, C. R. Lazaridis, and E. Kosmatopoulos, "Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations," Infrastructures, vol. 10, no. 5, Art. no. 114, 2025, doi: 10.3390/infrastructures10050114.
[2] M. Noaeen, A. Naik, L. Goodman, J. Crebo, T. Abrar, Z. Shakeri Hossein Abad, A. L. C. Bazzan, and B. Far, "Reinforcement learning in urban network traffic signal control: A systematic literature review," Expert Systems with Applications, vol. 199, Art. no. 116830, 2022, doi: 10.1016/j.eswa.2022.116830.
[3] A. Cabrejas-Egea, R. Zhang, and N. Walton, "Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems," Transportation Research Procedia, vol. 58, pp. 638-645, 2021, doi: 10.1016/j.trpro.2021.11.084.
[4] F. Mao, Z. Li, Y. Lin, and L. Li, "Mastering Arterial Traffic Signal Control With Multi-Agent Attention-Based Soft Actor-Critic Model," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3129-3144, 2023, doi: 10.1109/TITS.2022.3229477.
[5] S. Yang, "Hierarchical graph multi-agent reinforcement learning for traffic signal control," Information Sciences, vol. 634, pp. 55-72, 2023, doi: 10.1016/j.ins.2023.03.087.
[6] W. Fang, X. Zhao, and C. Zhang, "Fairness-aware multi-agent reinforcement learning and visual perception for adaptive traffic signal control," Optoelectronics Letters, vol. 20, pp. 764-768, 2024, doi: 10.1007/s11801-024-3267-2.
[7] M. Elharoun, S. M. El-Badawy, E. A.-E. Shwaly, and U. Elrawy Shahdah, "Adaptive traffic signal control using deep reinforcement learning: A multi-objective approach for single and multi-intersection scenarios," IATSS Research, vol. 49, no. 4, pp. 481-492, 2025, doi: 10.1016/j.iatssr.2025.10.004.