Leveraging Deep Learning for Traffic Management using Existing System

Urban traffic congestion is a pressing issue that worsens with the rise of vehicular density in cities. Traditional traffic management systems, based on fixed signal timings, lack the adaptability needed to handle the dynamic and often unpredictable nature of traffic flow. This project introduces an AI-based traffic signal control system that leverages real- time video data from CCTV cameras to optimize traffic signal timings based on actual traffic conditions dynamically. By integrating computer vision techniques and machine learning models, the system can calculate traffic density in real time and adjust traffic signal timings to reduce waiting times, fuel consumption, and pollution. The project aims to improve urban traffic management by providing a smart, adaptive, and scalable solution.