Adaptive Traffic Signal Control System Based on Future Traffic Environment

Abstract: Traffic congestion is a major challenge in urban environments due to increasing vehicle density and inefficient fixed-time traffic signal systems. Traditional traffic lights operate on predefined timers that do not consider real-time traffic conditions, resulting in unnecessary delays, increased fuel consumption, and higher carbon emissions. This paper proposes an Adaptive Traffic Signal Timer system using realtime computer vision and deep learning techniques to dynamically adjust traffic signal timings based on lane-wise vehicle density. The system employs the YOLO (You Only Look Once) object detection algorithm integrated with OpenCV to detect and count vehicles from live video streams. Based on calculated density metrics, optimal green signal durations are assigned dynamically. The proposed system improves traffic flow efficiency, reduces waiting time, and supports intelligent transportation and smart city infrastructure development.

Keywords: Intelligent Transportation System, Computer Vision, YOLO, Traffic Density Estimation, Adaptive Signal Control, Deep Learning.