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Scientific paper ID 2784 : 2026/4
COORDINATED TRAFFIC MANAGEMENT THROUGH ARTIFICIAL INTELLIGENCE
Vasil Koprivshtenov The growing urbanization and increased traffic intensity in modern cities pose serious challenges to traditional traffic management methods. Static and poorly adaptive systems often fail to respond effectively to dynamic and unpredictable traffic conditions. In this context, artificial intelligence is establishing itself as a key technology for building intelligent and coordinated transport systems.
This report examines the principles and application of coordinated traffic management through artificial intelligence, focusing on adaptive algorithms, real-time data analysis and synchronization between multiple intersections. Basic AI approaches are presented, as well as practical examples from real urban environments demonstrating significant reductions in congestion, travel times and harmful emissions. Finally, the main challenges and prospects for the development of intelligent transport systems as part of the concept of sustainable and ”smart” cities are discussed. координирано управление трафик изкуствен интелектcoordinated management traffic artificial intelligenceVasil Koprivshtenov BIBLIOGRAPHY [1] Farooqi, A., Dutta, R., Gocher, H., Bishnoi, A., Sharma, P., & Sharma, S. (2025). Artificial Intelligence in Traffic Management: A Survey on Techniques, Challenges, and Future Directions. In Recent Trends in Communication and Intelligent Systems. Springer. https://doi.org/10.1007/978-3-032-09832-0_5... [2] Zhao, Z., & Chen, J. (2025). Application of artificial intelligence technology in the economic development of urban intelligent transportation systems. PeerJ Computer Science, 11, e2728. https://doi.org/10.7717/peerj-cs.2728 [3] Zemmouchi Ghomari, L. (2025). Artificial intelligence in intelligent transportation systems. Journal of Infrastructure, Mobility and Smart Engineering, 6(1), 26–40. [4] El Mokhi, C., Erguig, H., Hmina, N., & Hachimi, H. (2025). Intelligent Traffic Management Systems: A Literature Review on AI Based Traffic Light Control. In The Future of Urban Living: Smart Cities and Sustainable Infrastructure Technologies. Springer. https://doi.org/10.1007/978-3-031-98334-4_1... [5] Agrahari, A., Dhabu, M. M., Deshpande, P. S., Tiwari, A., Baig, M. A., & Sawarkar, A. D. (2024). Artificial Intelligence Based Adaptive Traffic Signal Control System: A Comprehensive Review. Electronics, 13(19), 3875. https://doi.org/10.3390/electronics13193875... [6] Wang, Z., Wang, X., Wu, Z., Hu, J., Yuan, F., & Tian, Z. (2026). Traffic Flow Prediction in Intelligent Transportation Systems: A Comprehensive Review. Algorithms, 19(4), 310. https://doi.org/10.3390/a19040310 [7] Michailidis, P., Michailidis, I., Lazaridis, C. R., & Kosmatopoulos, E. (2025). Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations. Infrastructures, 10(5), 114. https://doi.org/10.3390/infrastructures1005... [8] Xiao, H., Li, H., Wang, Z., Yang, Z., Qi, S., Zhang, J., Cai, D., & Yin, J. (2026). Intelligent traffic signal control based on reinforcement learning: A survey. Artificial Intelligence Review, 59, Article 128. https://doi.org/10.1007/s10462-026-11530-9 ... [9] Balasubramanian, A. (2024). AI Driven Optimization of Urban Mobility: Integrating Autonomous Vehicles with Real Time Traffic and Infrastructure Analytics. San Jose State University. [10] Michailidis, P., Michailidis, I., Lazaridis, C. R., & Kosmatopoulos, E. (2025). Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations. Infrastructures, 10(5), 114. [11] Xiao, H., Li, H., Wang, Z., Yang, Z., Qi, S., Zhang, J., Cai, D., & Yin, J. (2026). Intelligent traffic signal control based on reinforcement learning: A survey. Artificial Intelligence Review, 59. [12] Tolani, M., Saathwik, G. E., Roy, A., Rao, D. B., Bajpai, A., & Balodi, A. (2025). Machine learning based adaptive traffic prediction and control using edge impulse platform. [13] Xiao, F., Lu, J., Li, L., Tu, W., & Li, C. (2024). Advances in reinforcement learning for traffic signal control: A review of recent progress. Intelligent Transportation Infrastructure, https://doi.org/10.1093/iti/liaf009 |
