Road Accident Detection Using Maching Learning

Road accidents are a major cause of fatalities worldwide, necessitating rapid detection and response systems. This project presents an IoT-based road accident detection system using machine learning to automatically identify accidents, assess their severity, and notify emergency services in real-time. The system integrates accelerometers, gyroscopes, GPS, and gas sensors with a microcontroller (ESP32/Raspberry Pi) to collect real-time vehicular data. A machine learning model, trained using Random Forest, SVM, or deep learning algorithms, processes sensor data to distinguish between normal driving behavior and accidents. Upon detecting an accident, the system triggers an alert mechanism, sending real- time location details via SMS, email, or cloud-based notifications to emergency contacts and rescue services. Additionally, a mobile or web application provides a user- friendly interface for monitoring accident reports. Key words: IoT, Machine Learning, Road Accident Detection, Smart Transportation, Emergency Response, Real-time Monitoring, GPS Tracking, Traffic Safety, AI in Transportation.