AI-Powered Smart Traffic Management System for Urban Congestion Reduction

ABSTRACT- This project showcases a real-time, AI-powered traffic signal management system that utilises intelligent signal control to enhance urban mobility and reduce traffic congestion. To dynamically control signal timings at intersections and monitor traffic density, the system integrates embedded electronics, computer vision, and machine learning. Traffic density is estimated in real-time by analysing live video feeds from cameras using deep learning models, such as YOLO (You Only Look Once), which are used for vehicle detection and counting. To monitor traffic patterns and gradually improve system accuracy, the data is processed and stored in a dedicated database. The system utilises heuristic algorithms or reinforcement learning techniques to optimise signal timings in real-time based on the number of vehicles. This system allows high-density lanes to have longer green lights. Hardware traffic lights are controlled by embedded controllers, such as Raspberry Pi or Arduino, based on AI-generated timing data. Additionally, for traffic monitoring and future scalability, the system provides a real-time web-based dashboard. In addition to reducing idle time and fuel usage, this project establishes the framework for smart city traffic infrastructure and enables the prioritisation of emergency vehicles.

Keywords- AI-powered traffic management, computer vision, deep learning, real-time signal control, smart city infrastructure.