International Journal of Transportation Engineering

International Journal of Transportation Engineering

Machine Learning based Distributed Traffic Signal Control

Document Type : Research Paper

Authors
Alireza Rezaee 1
Amirhossein Safdari 2
1 Associate Professor, Department of Mechatronics Engineering, School of Intelligent Systems Engineering, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
2 M.Sc., Department of Mechatronics Engineering, School of Intelligent Systems Engineering, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
10.22119/ijte.2025.523812.1697
Abstract
Optimizing traffic flow remains a central objective in transportation research. With the continuous growth in vehicle numbers, the limited scalability of existing infrastructure, and the inherently nonlinear, dynamic, and stochastic nature of traffic systems, signal control at road junctions has become an increasingly complex control challenge. Traditional signal control methods are predominantly rule-based, designed for deterministic environments, and often fail to adapt to real-time traffic variations and unexpected disruptions. This study proposes a distributed traffic signal control framework built upon a Machine Learning (ML) paradigm utilizing Reinforcement Learning (RL). Owing to their non-model-based architecture and computational efficiency, RL-based methods exhibit strong adaptability to changing traffic conditions and robustness against environmental uncertainties. In the proposed system, each junction is independently managed by an autonomous learning agent capable of interacting with its environment, refining its control policy over time, and making localized, real-time decisions. Simulation results demonstrate the effectiveness of the proposed approach, with vehicle queue lengths and average waiting times reduced by 35% to 66.4% on roads leading to the junctions, compared to conventional rule-based systems. These findings highlight the potential of distributed, learning-enabled control strategies in achieving scalable, adaptive, and efficient urban traffic management.
Keywords
Road Traffic
Distributed Control
Machine Learning
Reinforcement Learning
- Ahmed, S. K., Mohammed, M. G., Abdulqadir, S. O., El‐Kader, R. G. A., El‐Shall, N. A., Chandran, D., ... & Dhama, K. (2023). Road traffic accidental injuries and deaths: A neglected global health issue. Health science reports, 6(5), e1240.
 
- Rezaee, A., Safdari, A., & Fazli, M. J. (2024). Automatic Path Planning for GMAW Welding on Locomotive Bogie in Low Contrast by Welding Robot. International Journal of Robotics, Theory and Applications, 10(1), 22-31.
 
- Rizwan, P., Suresh, K., & Babu, M. R. (2016, October). Real-time smart traffic management system for smart cities by using Internet of Things and big data. In 2016 international conference on emerging technological trends (ICETT) (pp. 1-7). IEEE.
 
- Ata, A., Khan, M. A., Abbas, S., Ahmad, G., & Fatima, A. (2019). Modelling smart road traffic congestion control system using machine learning techniques. Neural Network World, 29(2), 99-110.
 
- Abbas, S., Khan, M. A., Athar, A., Shan, S. A., Saeed, A., & Alyas, T. (2022). Enabling smart city with intelligent congestion control using hops with a hybrid computational approach. The Computer Journal, 65(3), 484-494.
 
- Bokaba, T., Doorsamy, W., & Paul, B. S. (2022). A comparative study of ensemble models for predicting road traffic congestion. Applied Sciences, 12(3), 1337.
 
- Balaji, P. G., German, X., & Srinivasan, D. (2010). Urban traffic signal control using reinforcement learning agents. IET Intelligent Transport Systems, 4(3), 177-188.
 
- Alkhatib, A. A., Maria, K. A., AlZu'bi, S., & Maria, E. A. (2022). Smart traffic scheduling for crowded cities road networks. Egyptian Informatics Journal, 23(4), 163-176.
 
- Liu, Z. (2007). A survey of intelligence methods in urban traffic signal control. IJCSNS International Journal of Computer Science and Network Security, 7(7), 105-112.
- Qiao, J., Yang, N., & Gao, J. (2010). Two-stage fuzzy logic controller for signalized intersection. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(1), 178-184.
 
- Qadri, S. S. S. M., Gökçe, M. A., & Öner, E. (2020). State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review, 12, 1-23.
 
- Sun, G., Qi, R., Liu, Y., & Xu, F. (2024). A dynamic traffic signal scheduling system based on improved greedy algorithm. PLoS one, 19(3), e0298417.
 
- Bakker, B., Whiteson, S., Kester, L., & Groen, F. C. (2010). Traffic light control by multiagent reinforcement learning systems. In Interactive Collaborative Information Systems (pp. 475-510). Berlin, Heidelberg: Springer Berlin Heidelberg.
 
- Abdoos, M., Mozayani, N., & Bazzan, A. L. (2013). Holonic multi-agent system for traffic signals control. Engineering Applications of Artificial Intelligence, 26(5-6), 1575-1587.
 
- El-Tantawy, S., Abdulhai, B., & Abdelgawad, H. (2013). Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC): Methodology and large-scale application on downtown Toronto. IEEE transactions on intelligent transportation systems, 14(3), 1140-1150.
 
- Rezaee, A. (2016). Control of Road Traffic Using Learning Classifier System.
- Eom, M., & Kim, B. I. (2020). The traffic signal control problem for intersections: a review. European transport research review, 12, 1-20.
- De Oliveira, L. F. P., Manera, L. T., & Da Luz, P. D. G. (2020). Development of a smart traffic light control system with real-time monitoring. IEEE Internet of Things Journal, 8(5), 3384-3393.
 
- Reza, S., Oliveira, H. S., Machado, J. J., & Tavares, J. M. R. (2021). Urban safety: an image-processing and deep-learning-based intelligent traffic management and control system. Sensors, 21(22), 7705.
 
- Safdari, A., Zarafshan, P., Alipour, K., Tarvirdizadeh, B., & Fang, G. (2024, December). Machine Learning based Multiple Impedance Control of a Space Free-Flying Robot. In 2024 12th RSI International Conference on Robotics and Mechatronics (ICRoM) (pp. 001-007). IEEE.
 
- Safdari, A., Mansouri, A., Fazli, M. J., Zarafshan, P., Alipour, K., & Tarvirdizadeh, B. (2025, February). PID Controller Design for a Mechatronic System using Lion Optimization Algorithm. In 2025 Fifth National and the First International Conference on Applied Research in Electrical Engineering (AREE) (pp. 1-7). IEEE.
 
- Nguyen, T. T., Nguyen, N. D., & Nahavandi, S. (2020). Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications. IEEE transactions on cybernetics, 50(9), 3826-3839.
 
- Haydari, A., & Yılmaz, Y. (2020). Deep reinforcement learning for intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(1), 11-32.
 
- Fahad, M., Nagy, R., & Gosztola, D. (2022). Pavement sensing systems: literature review. Civil and Environmental Engineering Reports, 18(2), 603-630.
International Journal of Transportation Engineering
Volume 13, Issue 4 - Serial Number 52
Spring 2026
Pages 2277-2287