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ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 8 (2026): Volume 10, Issue 8, August 2026 | Pages: 1-8
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 07-08-2026
Traditional traffic signal systems operate using fixed-time intervals that are unable to adapt to varying traffic conditions, leading to traffic congestion, increased travel time, excessive fuel consumption, and higher carbon emissions. With the rapid growth of urban traffic, there is an increasing need for intelligent traffic management systems capable of making real-time decisions.
This paper presents an Intelligent Traffic Light Control System Using Computer Vision, which integrates Artificial Intelligence, Deep Learning, and real-time traffic analytics to optimize signal control and improve road efficiency. The proposed system utilizes the YOLOv8 object detection model to detect, classify, and count vehicles from live CCTV surveillance feeds with high accuracy. Based on the detected traffic density, a Dynamic Timer Algorithm dynamically allocates green signal durations to each lane, thereby reducing unnecessary waiting times and improving overall traffic flow. The system further incorporates automated red-light violation detection, capturing images of vehicles crossing the stop line during a red signal to support traffic law enforcement.
To enhance adaptability under varying environmental conditions, weather-based signal optimization is achieved using real-time weather data obtained through an online API. A user-friendly Tkinter-based graphical interface provides live traffic visualization, vehicle counts, signal status, weather information, violation records, and automated daily traffic reports. Experimental evaluation demonstrates that the proposed system significantly improves traffic flow, reduces congestion and vehicle waiting time, enhances road safety, and provides a scalable, cost-effective solution for deployment in modern Smart City and Intelligent Transportation System (ITS) environments.
Computer Vision, YOLOv8, Intelligent Traffic Management, Dynamic Traffic Signal Control, Vehicle Detection, Deep Learning, OpenCV, Red-Light Violation Detection, Smart Cities, Artificial Intelligence.
Kshitij Gangan, Aditya Bhosale, Harsh Kharwar, Rounak Gupta, & Dr. Smita Ganjare. (2026). Intelligent Traffic Light Control Using Computer Vision. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(8), 1-8. Article DOI https://doi.org/10.47001/IRJIET/2026.108001
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M. Ashkanani, A. AlAjmi, A. Alhayyan, Z. Esmael, M. AlBedaiwi, and M. Nadeem, “A Self-Adaptive Traffic Signal System Integrating Real-Time Vehicle Detection and License Plate Recognition for Enhanced Traffic Management,” Inventions, vol. 10, no. 1, Art. no. 14, 2025, doi: 10.3390/inventions10010014.
T. Azfar, K. Huang, A. Tracy, S. Misiewicz, C. Liu, and R. Ke, "Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning," Nature Communications, vol. 15, 2024, Art. no. 45427.
B. R. Kumar, N. Kumaran, J. U. Prakash, S. Salunkhe, R. Venkatesan, R. Shanmugam, and E. S. Abouel Nasr, "A Dynamic Traffic Light Control Algorithm to Mitigate Traffic Congestion in Metropolitan Areas," Sensors, vol. 24, no. 12, Art. no. 3987, 2024, doi: 10.3390/s24123987.
Wang, X., Jerome, Z., Wang, Z. et al. Traffic light optimization with low penetration rate vehicle trajectory data. Nat Commun 15, 1306 (2024). https://doi.org/10.1038/s41467-024-45427-4
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors," arXiv preprint arXiv:2207.02696, 2022, doi: 10.48550/arXiv.2207.02696.
T. Zhao, P. Wang and S. Li, "Traffic Signal Control with Deep Reinforcement Learning," 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS), Chongqing, China, 2019, pp. 763-767, doi: 10.1109/ICICAS48597.2019.00164.
G. Zheng, X. Zang, N. Xu, H. Wei, Z. Yu, V. Gayah, K. Xu, and Z. Li, "Diagnosing Reinforcement Learning for Traffic Signal Control," arXiv preprint arXiv:1905.04716, 2019, doi: 10.48550/arXiv.1905.04716.
Cambridge Systematics, Inc. and Virginia Tech Transportation Institute, Empirical Studies on Traffic Flow in Inclement Weather: Summary Report, Report No. FHWA-HOP-07-073, Federal Highway Administration, U.S. Department of Transportation, Washington, DC, USA, Oct. 2006.
N. Wojke, A. Bewley, and D. Paulus, "Simple Online and Realtime Tracking with a Deep Association Metric," arXiv preprint arXiv:1703.07402, 2017, doi: 10.48550/arXiv.1703.07402.
Zhang, H., Liang, M. & Wang, Y. YOLO-BS: a traffic sign detection algorithm based on YOLOv8. Sci Rep 15, 7558 (2025). https://doi.org/10.1038/s41598-025-88184-0
D. Rush, "Real-Time Traffic Management Is Reshaping Urban Mobility," Digi International Blog. Available: https://www.digi.com/blog/post/iot-monitor-traffic
S. Muruganandam, "Smart Traffic Management System with Real Time Analysis," Medium, Jan. 30, 2024.Available: https://medium.com/@sujithra828/smart-traffic-management-system-with-real-time-analysis-15d53c32316d
W. Wen, "A Dynamic and Automatic Traffic Light Control Expert System for Solving the Road Congestion Problem," Expert Systems with Applications, vol. 34, no. 4, pp. 2370–2381, May 2008, doi: 10.1016/j.eswa.2007.03.007.
Alharbi, A., Halikias, G., Sen, A.A.A. et al. A framework for dynamic smart traffic light management system. Int. j. inf. tecnol. 13, 1769–1776 (2021). https://doi.org/10.1007/s41870-021-00755-2
Nautiyal, K., Gangodkar, D., Diwakar, M. et al. Intelligent traffic light management using predictive and dynamic traffic flow analysis. Sci Rep 15, 37188 (2025). https://doi.org/10.1038/s41598-025-13694-w
K. H. N. Bui, O. J. Lee, J. J. Jung, and D. Camacho, "Dynamic Traffic Light Control System Based on Process Synchronization Among Connected Vehicles," in Ambient Intelligence – Software and Applications: 7th International Symposium on Ambient Intelligence (ISAmI 2016), H. Lindgren et al., Eds. Cham, Switzerland: Springer, 2016, pp. 85–94, vol. 476, Advances in Intelligent Systems and Computing, doi: 10.1007/978-3-319-40114-0_9.
J. T. G. Nodado, H. C. P. Morales, M. A. P. Abugan, J. L. Olisea, A. C. Aralar and P. J. M. Loresco, "Intelligent Traffic Light System Using Computer Vision with Android Monitoring and Control," TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South), 2018, pp. 2461-2466, doi: 10.1109/TENCON.2018.8650084.
V. Gorodokin, S. Zhankaziev, E. Shepeleva, K. Magdin, and S. Evtyukov, "Optimization of Adaptive Traffic Light Control Modes Based on Machine Vision," Transportation Research Procedia, vol. 57, pp. 241–249, 2021, doi: 10.1016/j.trpro.2021.09.047.
M. Abdoos, N. Mozayani and A. L. C. Bazzan, "Traffic light control in non-stationary environments based on multi agent Q-learning," 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA, 2011, pp. 1580-1585, doi: 10.1109/ITSC.2011.6083114.
M. M. Gandhi, D. S. Solanki, R. S. Daptardar and N. S. Baloorkar, "Smart Control of Traffic Light Using Artificial Intelligence," 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, 2020, pp. 1-6, doi: 10.1109/ICRAIE51050.2020.9358334.
V. Zinchenko, G. Kondratenko, I. Sidenko and Y. Kondratenko, "Computer Vision in Control and Optimization of Road Traffic," 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 2020, pp. 249-254, doi: 10.1109/DSMP47368.2020.9204329.
Á.Serrano, C. Conde, L. J. Rodríguez-Aragón, R. Montes, and E. Cabello, "Computer Vision Application: Real Time Smart Traffic Light," in Computer Aided Systems Theory – EUROCAST 2005, R. Moreno Díaz, F. Pichler, and A. Quesada Arencibia, Eds. Lecture Notes in Computer Science, vol. 3643. Berlin, Germany: Springer, 2005, pp. 673–680, doi: 10.1007/11556985_68.
M. F. Chowdhury, M. Ryad Ahmed Biplob and J. Uddin, "Real Time Traffic Density Measurement using Computer Vision and Dynamic Traffic Control," 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, 2018, pp. 353-356, doi: 10.1109/ICIEV.2018.8641039.
C. T. Barba, M. Á. Mateos, P. R. Soto, A. M. Mezher and M. A. Igartua, "Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights," 2012 IEEE Intelligent Vehicles Symposium, Madrid, Spain, 2012, pp. 902-907, doi: 10.1109/IVS.2012.6232229.
D. Saha, S. M. Sorif and P. Dutta, "Weather Adaptive Intelligent Street Lighting System With Automatic Fault Management Using Boltuino Platform," 2021 International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia, 2021, pp. 1-6, doi: 10.1109/ICISS53185.2021.9533234.
C. Olaverri-Monreal, P. Gomes, M. K. Silvéria and M. Ferreira, "In-Vehicle Virtual Traffic Lights: A graphical user interface," 7th Iberian Conference on Information Systems and Technologies (CISTI 2012), Madrid, Spain, 2012, pp. 1-6.
Á.Gonzalez et al., "Automatic Traffic Signs and Panels Inspection System Using Computer Vision," in IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 485-499, June 2011, doi: 10.1109/TITS.2010.2098029.
N. Hoose, "Computer Vision as a Traffic Surveillance Tool," IFAC Proceedings Volumes, vol. 23, no. 2, pp. 57–64, 1990, doi: 10.1016/S1474-6670(17)52650-6.
Krishna, M. Poddar, Giridhar M K, A. S. Prabhu and Umadevi V, "Automated traffic monitoring system using computer vision," 2016 International Conference on ICT in Business Industry & Government (ICTBIG), Indore, India, 2016, pp. 1-5, doi: 10.1109/ICTBIG.2016.7892717.
L. Zhang, Y. Zhang, S. Xie, J. Deng, and C. Li, “DynamicLight: Two-Stage Dynamic Traffic Signal Timing,” arXiv preprint arXiv:2211.01025, Nov. 2022, revised May 2024. doi: 10.48550/arXiv.2211.01025.
HAMBLI, C., & AMAD, M. (2026). A real-time adaptive traffic light control algorithm at urban intersections for smart cities. Applied Computer Science, 22(1), 20–34. https://doi.org/10.35784/acs_7569
R. Bharadwaj, J. Deepak, M. Baranitharan and V. V. Vaidehi, "Efficient dynamic traffic control system using wireless sensor networks," 2013 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, India, 2013, pp. 668-673, doi: 10.1109/ICRTIT.2013.6844280.
Jin Lei and U. Ozguner, "Combined decentralized multi-destination dynamic routing and real-time traffic light control for congested traffic networks," Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), Phoenix, AZ, USA, 1999, pp. 3277-3282 vol.4, doi: 10.1109/CDC.1999.827776.
S. K. Kumaran, S. Mohapatra, D. P. Dogra, P. P. Roy, and B.-G. Kim, “Computer Vision-Guided Intelligent Traffic Signaling for Isolated Intersections,” Expert Systems with Applications, vol. 134, pp. 267–278, Nov. 2019. doi: 10.1016/j.eswa.2019.05.049.