AI based Real-Time Traffic Signal Control System using Machine Learning

Abstract

This study presents a novel system that utilizes computer vision and machine learning approaches to address the problem of traffic congestion in urban areas. The proposed system leverages the advanced object detection algorithm, You Only Look Once (YOLO), to detect and track vehicles in live camera footage from traffic junctions. The system then calculates the traffic density in real-time by analyzing the number and speed of vehicles passing through the intersection. The proposed system utilizes an intelligent algorithm that optimizes traffic flow by switching traffic lights based on the calculated traffic density. This approach reduces congestion and minimizes delays, resulting in faster transit times and reduced fuel consumption and air pollution. To assess the performance of the proposed system, experiments are carried on real-world traffic data. The results demonstrate that the system can accurately detect and track vehicles with high precision and recall rates. The real-time traffic density calculations produced by the system were found to be highly reliable, and the traffic light switching algorithm led to a significant reduction in traffic congestion and improved traffic flow. The proposed system has several advantages over traditional traffic management systems, including lower implementation and maintenance costs, improved accuracy and efficiency, and the ability to adapt to changing traffic conditions in real-time.