10.57647/j.fomj.2025.0604.23

A fuzzy-based planning method and meta-heuristic optimization for traffic signal control

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  • Seyed Mehdi Seifivand1,
  • Parvaneh Asghari*2,
  • Hamid Haj Seyyed Javadi3,
  • Hamzehali Nourmohammadi4,
  1. Department of Computer Engineering, Bo.C., Islamic Azad Iran University, Borujard, Iran
  2. Department of Computer Engineering, CT.C., Islamic Azad University, Tehran, Iran
  3. Department of Computer Engineering, Shahed University, Tehran, Iran
  4. Department of Scientometrics, Shahed University, Tehran, Iran

Received: 2025-10-28

Revised: 2025-12-05

Accepted: 2025-12-24

Published in Issue 2025-12-30

Copyright (c) 2025 Seyed Mehdi Seifivand, Parvaneh Asghari, Hamid Haj Seyyed Javadi, Hamzehali Nourmohammadi (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Seifivand, S. M., Asghari, P., Haj Seyyed Javadi, H., & Nourmohammadi, H. (2025). A fuzzy-based planning method and meta-heuristic optimization for traffic signal control. Fuzzy Optimization and Modeling Journal (FOMJ), 6(4). https://doi.org/10.57647/j.fomj.2025.0604.23
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Abstract

To regulate traffic volume and average vehicle delay, traffic signal control systems aim to develop a computational model to examine vehicle lines on urban roadways. Based on the optimization of vehicle speed on each street and traffic light timing, a hybrid traffic signal control system based on fuzzy logic and wild horse optimization algorithm (HFWHO-TSC) is introduced in this study. The first phase is optimized using the wild horse meta-heuristic algorithm (WHO) technique, and the timing of the traffic lights is optimized using fuzzy logic depending on the traffic intensity of the nearby streets in various directions of the intersection. Additionally, an iterative loop is used in this work to find the ideal fuzzy time for traffic light lighting. The proposed hybrid system is compared with two other controller systems: a fixed-time signal controller and a signal controller that combines particle swarm optimization algorithms and fuzzy logic in a hybrid manner (HFPSO-TSC). With a performance gain of almost 37% over the fixed-time traffic controller and 20% over HFPSO-TSO, respectively, the HFWHO-TSC solution outperformed the others. The proposed hybrid system's performance effectively reduces both traffic congestion and driver annoyance brought on by signals. Additionally, it lessens fuel consumption and pollution.

Keywords

  • Traffic Signal Control (TSC) system; Fuzzy logic system; Wild Horse Optimization (WHO) algorithm; Particle Swarm Optimization (PSO) algorithm; Matlab software; Fuzzy time.
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References

  1. Z. Cakici and Y. S. Murat, “A differential evolution algorithm-based traffic control model for signalized intersections,” Advances in Civil Engineering, 2019(1):1–16, 2019.
  2. S. Sen, “Vehicles in Mumbai increase by more than half in seven years,” The Times of India, 201, 2014.
  3. N. S. A. Aziz and N. Noordin, “Fuzzy logic to optimize traffic signal control in a congested area,” Jurnal Intelek, 14(1):9–18, 2020.
  4. H. Mei, J. Li, B. Shi, and H. Wei, “Reinforcement learning approaches for traffic signal control under missing data,” arXiv preprint, arXiv:2304.10722, 2023.
  5. F. Rasheed, K.-L. A. Yau, and Y.-C. Low, “Deep reinforcement learning for traffic signal control under disturbances: A case study on Sunway City, Malaysia,” Future Generation Computer Systems, 109:431–445, 2020.
  6. C. Tan and K. Yang, “Privacy-preserving adaptive traffic signal control in a connected vehicle environment,” Transportation Research Part C: Emerging Technologies, 158:104453, 2024.
  7. I. Tunc and M. T. Soylemez, “Fuzzy logic and deep Q-learning based control for traffic lights,” Alexandria Engineering Journal, 67:343–359, 2023.
  8. M. Esmaeili, A. Anjomshoae, N. Shahsavari-Pour, P. Srisurin, and R. Banomyong, “An optimization similarity fuzzy inference method for traffic signal control at an isolated intersection,” Multimodal Transportation, 4(1):100234, 2025.
  9. D. Xu, C. Li, D. Wang, and G. Gao, “Robustness analysis of discrete state-based reinforcement learning models in traffic signal control,” IEEE Transactions on Intelligent Transportation Systems, 24(2):1727–1738, 2022.
  10. V. M. Madrigal Arteaga, J. R. Pérez Cruz, A. Hurtado-Beltrán, and J. Trumpold, “Efficient intersection management based on an adaptive fuzzy-logic traffic signal,” Applied Sciences, 12(12):6024, 2022.
  11. V. Yadav, J. P. Tripathi, and B. K. Thakur, “Traffic light optimization using fuzzy logic and genetic algorithm,” Mathematical Statistician and Engineering Applications, 71(4):10705–10712, 2022.
  12. L. Miao and D. Leitner, “Adaptive traffic light control with quality-of-service provisioning for connected and automated vehicles at isolated intersections,” IEEE Access, 9:39897–39909, 2021.
  13. T. Wang, J. Cao, and A. Hussain, “Adaptive traffic signal control for large-scale scenario with cooperative group-based multi-agent reinforcement learning,” Transportation Research Part C: Emerging Technologies, 125:103046, 2021.
  14. A. Ikidid, E. F. Abdelaziz, and M. Sadgal, “Multi-agent and fuzzy inference-based framework for traffic light optimization,” in Proc. 2023 Int. Conf. on Advances in Computing, Communication and Applied Informatics, 2023.
  15. Y. Chen and C. G. Cassandras, “Scalable adaptive traffic light control over a traffic network including transit delays,” in Proc. 2023 IEEE 26th Int. Conf. on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 1651–1656.
  16. I. Briki, R. Ellaia, and M. A. Chentoufi, “Fuzzy-TOPSIS decision-support framework for traffic signal control,” Innovative Infrastructure Solutions, 10(7):284, 2025.
  17. H. Zhang, Z. Fang, Y. Chen, H. Dai, Q. Jiang, and X. Zeng, “Traffic signal optimization control method based on attention mechanism updated weights double deep Q network,” Complex & Intelligent Systems, 11(5):217, 2025.
  18. H. Lin, Y. Han, W. Cai, and B. Jin, “Traffic signal optimization based on fuzzy control and differential evolution algorithm,” IEEE Transactions on Intelligent Transportation Systems, 24(2):1713–1726, 2022.
  19. X. Zhang, X. Fan, S. Yu, A. Shan, S. Fan, Y. Xiao, et al., “Intersection signal timing optimization: A multi-objective evolutionary algorithm,” Sustainability, 14(3):1506, 2022.
  20. Z. Islam, M. Abdel-Aty, and N. Mahmoud, “Using CNN-LSTM to predict signal phasing and timing aided by high-resolution detector data,” Transportation Research Part C: Emerging Technologies, 141:103742, 2022.
  21. M. Liu, J. Zhao, S. Hoogendoorn, and M. Wang, “A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersections,” Transportation Research Part C: Emerging Technologies, 134:103459, 2022.
  22. W. Zhao, Y. Ye, J. Ding, T. Wang, T. Wei, and M. Chen, “Ipdalight: Intensity- and phase duration-aware traffic signal control based on reinforcement learning,” Journal of Systems Architecture, 123:102374, 2022.
  23. Y. Wu, “Enhancing urban traffic flow through fuzzy logic-based signal light control optimization,” International Journal of e-Collaboration (IJeC), 20(1):1–3, 2024.
  24. P. Koonce, Traffic Signal Timing Manual. Washington, DC, USA: Federal Highway Administration, 2008.
  25. S. Sunkari, “The benefits of retiming traffic signals,” ITE Journal, 74(4):26–29, 2004.
  26. H. Homaei, S. Hejazi, and S. A. M. Dehghan, “A new traffic light controller using fuzzy logic for a full single junction involving emergency vehicle preemption,” Journal of Uncertain Systems, 9(1):49–61, 2015.
  27. M. Arora and D. Banga, “Application of morphological method of edge detection and fuzzy logic technique for traffic light control,” International Journal for Science and Emerging Technologies with Latest Trends, 1(1):31–36, 2012.
  28. I. Naruei and F. Keynia, “Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems,” Engineering with Computers, 38(4):3025–3056, 2022.
  29. S. Bekesiene, R. Smaliukiene, and R. Vaicaitiene, “Using ANNs in predicting the level of stress among military conscripts,” Mathematics, 9(6):626, 2021.
  30. E. Korkmaz and A. P. Akgüngör, “A hybrid traffic controller system based on flower pollination algorithm and type-2 fuzzy logic optimized with crow search algorithm for signalized intersections,” Soft Computing, 28(11):7227–7249, 2024.
  31. G. Sun, R. Qi, Y. Liu, and F. Xu, “A dynamic traffic signal scheduling system based on improved greedy algorithm,” Plos One, 19(2): e0298417, 2024.