Citation

Azhar A, Ariff NM, Bakar MAA, Roslan A. Sustainability (Basel) 2022; 14(7): e4101.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/su14074101

PMID

unavailable

Abstract

Accidents involving heavy vehicles are of significant concern as it poses a higher risk of fatality to both heavy vehicle drivers and other road users. This study is carried out based on the heavy vehicle crash data of 2014, extracted from the MIROS Road Accident and Analysis and Database System (M-ROADS). The main objective of this study is to identify significant variables associated with categories of injury severity as well as classify and predict heavy vehicle drivers' injury severity in Malaysia using the classification and regression tree (CART) and random forest (RF) methods. Both CART and RF found that types of collision, driver errors, number of vehicles involved, driver's age, lighting condition and types of heavy vehicle are significant factors in predicting the severity of heavy vehicle drivers' injuries. Both models are comparable, but the RF classifier achieved slightly better accuracy. This study implies that the variables associated with categories of injury severity can be referred by road safety practitioners to plan for the best measures needed in reducing road fatalities, especially among heavy vehicle drivers.

Language: en

Keywords

classification and regression tree; driver injury severity; heavy vehicles accident; machine learning; random forest