Smart Traffic Control System Using YOLO Algorithm
DOI:
https://doi.org/10.4108/eetismla.12670Keywords:
traffic management, YOLO, OpenCV, Python, computer vision, vehicle detection, smart citiesAbstract
Traffic congestion is one of the most serious challenges faced by developing countries, significantly affecting economic productivity and environmental sustainability. Most conventional traffic signal systems operate using fixed-time control strategies and do not adapt to real-time road conditions, leading to traffic congestion and inefficient utilization of road capacity. To address this issue, this study proposes an adaptive traffic signal control system that dynamically adjusts signal timings based on real-time traffic density.
The proposed system employs a YOLO-based vehicle detection model integrated with OpenCV to analyze live traffic video streams and perform lane-wise vehicle counting. Based on the detected traffic density, green signal durations are computed dynamically to optimize traffic flow across multiple lanes. Experimental evaluation demonstrates that the system achieves approximately 93% vehicle detection accuracy and reduces average waiting time by nearly 30% compared to traditional fixed-time signal control systems. The results indicate improved traffic distribution, reduced idle time, and enhanced overall junction efficiency, demonstrating the practical applicability of the proposed approach for intelligent transportation systems and smart city environments.
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