Application of machine learning technique for prediction of road accidents in Haryana-A novel approach

Abstract

Over the last few years, road accidents in developing countries are increasing at an alarming rate. In India, almost 3% of GDP is getting wasted in road accidents, which not only cause social problems but, also, imposes a huge burden on the Indian economy. Various researches have been done to analyze this situation using different methods and techniques on different stretches and intersections. This paper makes one of the first attempts to develop an Accident Prediction Model (APM) in the Indian State of Haryana. This study describes the procedure for collection and analysis of accident data, as well as the detailed methodology used to develop APMs. The Models were developed using one of the most common algorithms of machine learning i.e. linear regression technique. Results obtained from APM of Haryana State were compared with the results given by some of the highly successful APMs like Smeed’s Model, Valli’s Model and their comparisons were discussed to find the most efficient model. It was observed that the proposed model shows highly accurate results in predicting road accidents in Haryana. The output of this work can be used for theoretical as well as practical applications like road safety management for improving existing conditions of the road network in Haryana and to regulate new traffic safety policies in the future.