Machine learning based IoT System for Accident Detection and Prevention using GPS and GSM for Vehicles

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Abstract

This paper presents a novel approach to traffic accidents using advanced machine learning algorithms and an IoT-based system. Traffic accidents cause serious injuries, deaths, and financial damages worldwide. Preventing accidents involves quick notice and action. This system uses machine learning models and IoT sensors like accelerometers and GPS sensors to detect and analyze accidents in real time. IoT gadgets in cars track position and acceleration. The system alarms and sends data to emergency services or cloud platforms like Thing Speak when it detects abrupt changes indicating an accident. Sensor data is analyzed by machine learning algorithms trained on past accident datasets to identify accident severity and prioritize response efforts. The system's capacity to sound warnings quickly, properly assess severity, and adapt to different vehicle types and environments is its strength. The system uses IoT and machine learning to improve emergency response and reduce response times to save lives. Emergency responders, drivers, and policymakers may profit from its development by enhancing accident detection and response. It also reveals accident patterns and trends, helping improve road safety and avoid future incidents.

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Keywords

GPS sensors, IoT; Machine learning; prevent accidents; Thing speak.