Prediction of Road Accidents in the Different States of India using Machine Learning Algorithms

Abstract

Analysis of road accidents acting a significant task in the road transport system. This article, predicting road accidents based on four attributes, that is., collision type, road type, location, and weather. A machine learning model with a Random Forest Regressor and Decision Tree Regressor is developed and working to predict collisions based on collision records that have taken place in the different states of India. The hit and run, head-on collision, hit pedestrian, fog, cloudy, rainy, single lane, two-lane, four-lane, school, pedestrian crossing, market, and other parameters considered for analyzing and visualization of accidents in different states of India. The correlation between mortal rate and other features including different road conditions, conditions of weather, location, nature of collision and time of occurrence, kinds of motor vehicles involved in accidents were analyzed. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics are considered for predicting road accidents based on a greater number of parameters. The accuracy of the Random Forest Regressor model and Decision Tree Regressor model based on head-on collision is 94.2% and 84.8%, 92.8% and 96.9% for fog weather conditions, 80% and 81.5% based on single-lane accidents, and 90.3% and 86.1% based on pedestrian crossing attribute. The outcomes of the relative analysis proved that the Random Forest Regressor (RFR) model does better than the Decision Tree Regressor (DTR) model.