Intelligent Traffic Monitoring and Autonomous Navigation System with Real-Time Weather and Incident Alerts

In the era of intelligent transportation systems, real-time traffic and weather analysis play a critical role in ensuring safe and efficient navigation. This paper presents an automated route navigation and traffic alert system that integrates multiple APIs, including MapBox for dynamic mapping and OpenWeatherMap for weather analysis, to enhance road safety and travel efficiency. The system predicts traffic congestion, monitors weather conditions, and provides alternative routes using real-time data. Additionally, it incorporates an automated incident detection mechanism through a camera-enabled Raspberry Pi module, which records traffic anomalies and sends instant alerts to users. Our approach employs geospatial analytics, IoT-driven automation, and cloud-based APIs to offer proactive traffic management and enhanced situational awareness. By continuously monitoring road conditions, accidents, and weather disruptions, the system ensures optimized route selection and timely alerts. Furthermore, the traffic prediction model, combined with weather parameters, helps reduce unexpected delays and enhances navigation efficiency. Here we are trying to adapt it to the rural areas where there are roads that are unnamed and undefined. Here, It can be fixed on the Traffic Signal Pole where it records and reads the Intensity of Traffic Congestion and provides the optimal ETA and Route for the User. This research contributes to the advancement of intelligent transportation and automation technologies, addressing urban traffic challenges with a data-driven and predictive approach.