Design of Adaptive Traffic Light Control System Using Fuzzy Logic, Based on Number of Vehicles, Length of Queue and Types of Vehicles
Keywords:
traffic light, fuzzy logic, Mamdani, vehicle type, traffic congestionAbstract
Congestion at signalized intersections is often triggered by the use of fixed-time controllers that are unresponsive to the dynamics of traffic volume, queue length, and vehicle heterogeneity. This study aims to design an adaptive traffic light control system based on a Mamdani-type Fuzzy Logic Controller (FLC) by integrating three input variables: the number of vehicles, queue length, and vehicle type/size. The novelty of this research lies in the use of the vehicle type variable as an explicit input within the FLC model. The system was designed by determining the universe of discourse, membership functions, and a base of 27 IF-THEN rules, which were then implemented in Python using the scikit-fuzzy library through MIN-MAX inference mechanisms and Centroid of Area (COA) defuzzification. Testing was conducted on 27 input combinations representing traffic conditions ranging from low to heavy. The simulation results show that the generated green light duration ranges from 5.09 to 91.60 seconds, with a tendency to increase alongside the rise in the number of vehicles, queue length, and vehicle size. Thus, the proposed model is capable of generating green light duration decisions that are more responsive and representative of mixed traffic conditions.
References
Z. Fahrunnisa, Rahmadwati, and R. A. Setyawan, “Adaptive traffic light signal control using fuzzy logic based on real-time vehicle detection from video surveillance,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 10, no. 2, pp. 235–251, 2024. https://doi.org/10.26555/jiteki.v10i2.28712
K. Nautiyal, D. Gangodkar, and M. Diwakar, “Analysis on advancements in adaptive traffic lights system,” Procedia Computer Science, vol. 258, pp. 2767–2776, 2025. https://doi.org/10.1016/j.procs.2025.04.537
C. Dumitrescu, P. Ciotirnae, and C. Vizitiu, “Fuzzy logic for intelligent control system using soft computing applications,” Sensors, vol. 21, no. 8, p. 2617, 2021. https://doi.org/10.3390/s21082617
S. Prakash and R. Menon, “Multi-criteria fuzzy logic decision support for traffic signal optimization in mixed traffic environments,” IEEE Access, vol. 11, pp. 48501–48515, 2023.
T. D. Chala and L. T. Kóczy, "Agent-based intelligent fuzzy traffic signal control system for multiple road intersection systems," Mathematics, vol. 13, no. 1, pp. 1-27, 2024. https://doi.org/10.3390/math13010124
Savithramma R M and R. Sumathi, "Intelligent traffic signal controller for heterogeneous traffic using reinforcement learning," Green Energy and Intelligent Transportation, vol. 2, no. 6, p. 100124, Dec. 2023, https://doi.org/10.1016/j.geits.2023.100124
X. Zhang and K. Liu, “Deep learning-assisted fuzzy control for traffic signal coordination in urban corridors,” Transportation Research Part C, vol. 142, p. 103821, 2022.
Y. Zhang, Z. Guo, J. Wu, Y. Tian, H. Tang, and X. Guo, "Real-time vehicle detection based on improved YOLOv5," Sustainability, vol. 14, no. 19, p. 12274, Sep. 2022. https://doi.org/10.3390/su141912274
K. Zhu, H. Lyu, and Y. Qin, "Enhanced detection of small and occluded road vehicle targets using improved YOLOv5," Signal, Image and Video Processing, vol. 19, no. 168, Dec. 2024. https://doi.org/10.1007/s11760-024-03756-3
P. Rosayyan, J. Paul, S. Subramaniam, and S. I. Ganesan, "An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors," Measurement: Sensors, vol. 26, p. 100697, Apr. 2023. https://doi.org/10.1016/j.measen.2023.100697
Y. Y. Chen, J. Y. Wang, S. C. Lo, and W. T. Sung, "An emergency vehicle traffic signal preemption system considering queue spillbacks along routes and negative impacts on non-priority traffic," IET Intelligent Transport Systems, vol. 18, no. 8, pp. 1385-1395, 2024. https://doi.org/10.1049/itr2.12518
Tunc, A. Y. Yesilyurt, and M. T. Soylemez, "Different fuzzy logic control strategies for traffic signal timing control with state inputs," IFAC-PapersOnLine, vol. 54, no. 2, pp. 265-270, 2021. https://doi.org/10.1016/j.ifacol.2021.06.059
Y. Bi, Q. Ding, Y. Du, D. Liu, and S. Ren, "Intelligent traffic control decision-making based on type-2 fuzzy and reinforcement learning," Electronics, vol. 13, no. 19, p. 3894, Oct. 2024. https://doi.org/10.3390/electronics13193894
T. Meepokgit and S. Wisayataksin, "Traffic signal control with state-optimizing deep reinforcement learning and fuzzy logic," Applied Sciences, vol. 14, no. 17, p. 7908, Sep. 2024. https://doi.org/10.3390/app14177908
M. Vlasceanu et al., "Comparative evaluation of fuzzy logic and Q-learning for adaptive urban traffic signal control," Electronics, vol. 14, no. 14, p. 2759, Jul. 2025. https://doi.org/10.3390/electronics14142759
S. M. Seifivand, P. Asghari, H. H. S. Javadi, et al., "Optimizing and managing the lighting time of the traffic light using the reinforcement learning system based on fuzzy logic and training the system with evolutionary algorithms," International Journal of Intelligent Transportation Systems Research, vol. 23, pp. 1125–1144, Aug. 2025. https://doi.org/10.1007/s13177-025-00504-w
F. Zahwa, M. Simic, and C. T. Cheng, "Fuzzy-based traffic light control strategy: the good, the bad, and the ugly," in Big Data Analytics and Data Science, pp. 61–75, 2024. https://doi.org/10.1007/978-981-97-8666-4_6
T. D. Chala and L. T. Kóczy, "Agent-Based Intelligent Fuzzy Traffic Signal Control System for Multiple Road Intersection Systems," Mathematics, vol. 13, no. 1, 2025. https://doi.org/10.3390/math13010124
S. Furqon, I. Santoso, and Y. A. A. Soetrisno, “Perancangan sistem pengontrolan lampu lalu lintas menggunakan metode fuzzy,” Transient, vol. 9, no. 1, pp. 88–96, 2020. https://doi.org/10.14710/transient.v9i1.88-96
Z. Wang, "Queue storage design for metered on-ramps," International Journal of Transportation Science and Technology, vol. 2, no. 1, pp. 47–64, 2013. https://doi.org/10.1260/2046-0430.2.1.47
N. Tanarubun, N. Hartatik, and M. Firmansyah, "Performance analysis of unsignalized intersections on the Taman Asri Tambak Rejo Road in Sidoarjo Regency using the PKJI 2014 Method," Jurnal Indonesia Sosial Sains, vol. 6, no. 12, Dec. 2025. [Online]. Available: http://jiss.publikasiindonesia.id/.
H. K. An, M. Awais Javeed, G. Bae, N. Zubair, A. S. M. Metwally, P. Bocchetta, F. Na, and M. S. Javed, "optimized intersection signal timing : An intelligent approach-based study for sustainable models," Sustainability, vol. 14, no. 18, p. 11422, Sep. 2022. https://doi.org/10.3390/su141811422
J. Niittymäki, "Using Fuzzy Logic to Optimize Adaptive Traffic Signal Control," Transportation Research Part C: Emerging Technologies, vol. 9, no. 6, pp. 385-403, Des. 2001. https://doi.org/10.1016/S0968-090X(00)00045-3
C. P. Pappis and E. H. Mamdani, "A fuzzy logic controller for a traffic junction," IEEE Transactions on Systems, Man, and Cybernetics, vol. 7, no. 10, pp. 707-717, 1977. https://doi.org/10.1109/TSMC.1977.4309605.
J. P. Niittymäki, "Fuzzy traffic signal control," in Transportation Planning, M. Patriksson and M. Labbé, Eds., Boston, MA: Springer, pp. 185-211, 2002. https://doi.org/10.1007/0-306-48220-7_10
Federal Highway Administration (FHWA), "Operational Analysis Fundamentals," in Traffic Signal Timing Manual, 2nd ed., Washington, DC: U.S. Department of Transportation, 2013, ch. 3. [Online]. Available: https://ops.fhwa.dot.gov/publications/fhwahop13006/ch3.htm
M. S. Ghanim and G. Abu-Lebdeh, "Characterization of heavy vehicle headways in oversaturated interrupted conditions: Towards development of passenger car equivalency factors," International Journal of Transportation Science and Technology, vol. 11, no. 3, pp. 589-602, Sep. 2022. https://doi.org/10.1016/j.ijtst.2021.07.002.
J. Niittymäki, "Fuzzy traffic signal control-fuzzification and defuzzification methods, extended abstract," in Proc. 6th Meeting of the EURO Working Group on Transportation, Gothenburg, 1998, p. 5. [Online]. Available: https://tiedejatutkimus.fi/fi/results/publication/0359663998
J. Niittymäki, "Fuzzy Traffic Signal Control," in Transportation Planning: State of the Art, M. Patriksson and M. Labbé, Eds., Dordrecht, the Netherlands: Kluwer Academic Publishers, pp. 163-175, 2002. [Online]. Available: https://link.springer.com/chapter/10.1007/0-306-48220-7_10
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