Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest

Aziemah Azhar, Noratiqah Mohd Ariff, Mohd Aftar Abu Bakar and Azzuhana Roslan
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Aziemah Azhar: Vehicle Safety and Biomechanics Research Centre (VSB), Malaysian Institute of Road Safety Research (MIROS), Kajang 43000, Selangor, Malaysia
Noratiqah Mohd Ariff: Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Bandar Baru Bangi 43600, Selangor, Malaysia
Mohd Aftar Abu Bakar: Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Bandar Baru Bangi 43600, Selangor, Malaysia
Azzuhana Roslan: Crash Data Operational & Management Unit (CRADOM), Malaysian Institute of Road Safety Research (MIROS), Kajang 43000, Selangor, Malaysia

Sustainability, 2022, vol. 14, issue 7, 1-19

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.

Keywords: classification and regression tree; driver injury severity; heavy vehicles accident; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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