Real-Time Crossroad Traffic Light Management

Urban traffic congestion at intersections remains a critical challenge, leading to increased travel times, fuel consumption, and environmental pollution. Traditional traffic light systems, with their fixed or semi-actuated timing plans, often fail to adapt to the dynamic nature of traffic flow. This paper aims to provides a conceptual traffic light system and its logic for efficient traffic light management, utilizing real-time image recognition and machine learning technologies. High-resolution cameras are strategically placed at a 4-way intersection to continuously monitor and count various vehicle types, including cars, trucks, and motorcycles. Leveraging advanced object detection models, such as YOLO (You Only Look Once) or the Faster R-CNN, the system processes video feeds in real-time, accurately determining vehicle density on each road. The system dynamically adjusts green light durations based on vehicle presence and lane availability, optimizing traffic flow and minimizing congestion. A maximum green light duration is set to ensure efficient and fairness in traffic movement while preventing excessive delays. Furthermore, the system integrates an emergency vehicle detection feature using RF signal technology. When an emergency vehicle is detected, the system overrides the regular traffic sequence, providing a prioritized green light to ensure swift passage and reduce response times, thereby enhancing public safety. This research underscores the feasibility and effectiveness of incorporating real-time image recognition into traffic management systems while providing a concrete operational logic.