Title: Analysis on road crash severity of drivers using machine learning techniques

Authors: Mohit Mittal; Swadha Gupta; Shaifali Chauhan; Lalit Kumar Saraswat

Addresses: INRIA, Nord Europe, Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL), Lille, France ' Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India ' Department of Management, Prestige Institute of Management, Gwalior, India ' Department of Computer Science and Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad, India

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.

Keywords: injury severity; collision data; fatal accidents; machine learning.

DOI: 10.1504/IJESMS.2022.123344

International Journal of Engineering Systems Modelling and Simulation, 2022 Vol.13 No.2, pp.154 - 163

Received: 18 Jun 2021
Accepted: 18 Jul 2021
Published online: 10 Jun 2022 *

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