Analysis on road crash severity of drivers using machine learning techniques. (6th June 2022)

• Record Type: Journal Article
• Authors: Mittal, Mohit; Gupta, Swadha; Chauhan, Shaifali; Saraswat, Lalit Kumar
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 2(2022)
• Page Start: 154
• Page End: 163
• Publication Date: 2022-06-06
• Subjects: injury severity -- collision data -- fatal accidents -- machine learning; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Traffic accidents are significant general well-being concerns, bringing a large number of deaths and injuries around the globe. To improve driving safety, the examination of traffic data is basic to discover factors that are firmly identified with lethal mishaps. In this paper, our main objective to evaluate the severity based on various factor to reduce the road accidents and enhance the safety. Therefore, a long range of factors are considered to evaluate severity into two types, either fatal severity or non-fatal severity. Out of all the factors, we have evaluated the top ten features that are most important with the help of CART, random forest and XGBoost algorithm. For prediction of severity, we have considered the logistic regression, ridge regression and support vector machine regression. The experimental results show that fatal severity is higher for fog weather condition, heavy vehicles such as truck, male drivers and old age drivers.