Machine Learning and Gis Based Accident Detection and Prevention System for Powered Two Wheelers

Abstract

Two-wheeler accidents in most populated and developing countries have become so vulnerable and six accidents happen every hour on average. This research work proposes an efficient automatic accident detection system that attempts to detect the occurrences of accidents in Powered Two-Wheelers (PTW) automatically using vehicle-dependent parameters and the physiological parameters of the rider in real-time. The proposed system builds an accident detection system in PTW using three steps namely, critical event detection system, accident detection system, and severity assessment system. The critical event detection system reads the accelerometer sensor values from the On-Board Diagnostic (OBD) unit mounted on the PTW and classifies the state of the vehicle as normal, fall-like, and fall through the modified decision tree algorithm. The modified decision tree algorithm uses a tanh function to calculate entropy values newline