Title: A data-driven approach to assessing and analysing contributing factors to the severity of road accident injuries based on decision-making styles

Authors: Ali Ghazizadeh; Mahdi Hamid; Zahra Mehdizadeh Somarin; Behnaz Salimi

Addresses: School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran ' School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran ' School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran ' School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract: Road accidents are among the important challenges that all communities face. Fatal road accidents are closely related to the severity of the collision, which is affected by a variety of factors, including human error, road conditions and weather conditions. Individual errors are a significant cause of accidents, which are influenced by an individual's behavioural characteristics. Behavioural factors are developed by an individual's decision-making style. One of the innovative aspects of this study is the inclusion of drivers' decision-making styles in analysing the causes of accidents with severe injuries. Additionally, this study will determine the primary factors that contribute to the severity of injury utilising principal factor analysis and correlation matrix approaches. Our results show that flexible and hierarchical decision-making styles are the dominant styles in all groups. Non-observance of safe distance, vehicle defects, and bad weather are the three foremost factors affecting the severity of injuries. Human factors with nearly 60% importance, is the most important group of features in determining injury severity. Furthermore, the features of not wearing a seat belt, using a mobile phone while driving, and not observing a safe distance have the most positive alignment with injury, respectively.

Keywords: road accident; decision-making style; injury severity; principal component analysis; PCA; correlation matrix.

DOI: 10.1504/IJHFE.2022.126123

International Journal of Human Factors and Ergonomics, 2022 Vol.9 No.3, pp.231 - 260

Received: 21 Nov 2021
Accepted: 11 Mar 2022
Published online: 11 Oct 2022 *

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