Computer Vision-Based Adaptive Traffic Signal Control System | IJCT Volume 13 – Issue 1 | IJCT-V13I1P30
International Journal of Computer Techniques
ISSN 2394-2231
Author
Harshita Jain, Shravani Thete, Rutika Shahare, Pratiksha Kumbhare
Abstract
Urban traffic congestion is increasing rapidly due to the growing number of vehicles and the continued use of fixed-time traffic signal systems. Conventional traffic signals operate with predefined timings and do not consider real-time traffic conditions, which results in longer waiting time and inefficient traffic flow. This paper presents a computer vision-based adaptive traffic signal control system that dynamically adjusts signal timings according to real-time traffic density. Vehicle detection and counting are performed using the YOLO object detection model with OpenCV. Lane-wise traffic density is calculated and used to control signal duration automatically. Experimental observations show that the proposed system improves traffic movement and reduces congestion when compared with traditional fixed-time traffic signal systems.
Keywords
Adaptive Traffic Signal, Computer Vision, YOLO, OpenCV, Traffic Analysis.
Conclusion
This paper presented a computer vision- based adaptive traffic signal control system using YOLO and OpenCV. By adjusting signal timings based on real-time traffic density, the proposed system improves traffic efficiency and minimizes congestion. The system provides a practical solution for intelligent traffic management in urban environments.
References
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N. Haji, “A comprehensive review of vehicle detection using computer vision”, TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 19, no. 3, pp. 838-850, 2021. [5]M. Sharma, A. Bansal, V. Kashyap, P. Goyal, and T. Sheikh, “Intelligent Traffic Light Control System Based on Traffic Environment Using Deep Learning” in Proceedings of the International Conference on Emerging Technologies, 2020. [6]T. Sable, N. Parate, D. Nadkar, and S. Shinde, “Density and Time-based Traffic Control System using Video Processing”, ITM Web of Conferences, vol. 32, 2020. V. Zinchenko, G. Kondratenko, I. Sidenko, and Y. Kondratenko, “Computer Vision in Control and Optimization of Road Traffic”, IEEE Third International Conference on Data Stream Mining & Processing (DSMP), pp. 249-254, 2020.
How to Cite This Paper
Harshita Jain, Shravani Thete,
Rutika Shahare, Pratiksha Kumbhare (2025). Computer Vision-Based Adaptive Traffic Signal Control System. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
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