AI Based Solution for Traffic Management with Navigo Application

Our project focuses on developing a smart, AI-driven traffic management system that uses real-time data from traffic cameras, sensors and GPS to optimize urban mobility. By integrating machine learning algorithms, the system dynamically adjusts traffic signal timings based on traffic density, vehicle counts, and road conditions. Leveraging YOLO for vehicle detection OpenCV for image processing, and Google Maps API for traffic visualization, we provide real-time updates and route optimization to reduce traffic congestion.

The system includes predictive models that forecast potential traffic jams, helping to proactively prevent gridlock and reduce fuel consumption. A user-friendly mobile application built with Flutter, offers drivers alternate routes, while a comprehensive dashboard provides authorities with analytics for long-term infrastructure planning. The goal is to enhance traffic efficiency decrease travel time and promote sustainable urban mobility.