Enhancing Urban Traffic Management with Real-Time Computer Vision and AI: A Smart Traffic Signal System

This research presents the development and deployment of a smart traffic signal system utilizing computer vision technology to optimize traffic management. Employing a pretrained YOLO model and Python's OpenCV library, the system achieves real-time vehicle and lane detection, facilitating dynamic adjustments to traffic signals based on actual traffic conditions. Key features include multi-lane vehicle counting, emergency vehicle prioritization, and the deployment of motorized traffic barriers, which reduce human intervention and streamline traffic flow. The system incorporates an optimization algorithm that efficiently manages lane availability based on traffic data, aiding in the reduction of road congestion. Both simulation and realworld testing have been conducted to validate the system's effectiveness in enhancing road safety and managing congestion. Additionally, the study explores the scalability of the solution to larger urban environments and addresses various technical challenges faced during implementation.