Traffic Prediction and Management System Using Deep Learning

Abstract: This paper presents a real-time traffic prediction and navigation system that integrates GPS-based vehicle tracking with Google Maps API, TomTom Traffic API, and Weather APIs to enhance route optimization and safety. The proposed system dynamically updates routes based on live traffic and weather conditions, while providing users with real-time notifications about potential hazards. Algorithms such as Dijkstra, A*, Bellman-Ford, Kalman Filter, and K-Means Clustering are employed to ensure efficient routing and accurate vehicle tracking. The solution is tested using realistic scenarios and validated for reliability, responsiveness, and user experience.

Keywords: Real-time GPS, traffic prediction, route optimization, weather API, traffic API, Kalman Filter, A* algorithm, vehicle tracking