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

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.

Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia

“…This means that if we need to use these variables, we should select only one that will be more appropriate for the following estimations. [14]; Islam and Mannering (2021) [16]; Azhar et al (2022) [20] hp We observed a high correlation (equals more than 0.7) between 2 pairs of variables: "avp" and "hp" and "acb" and "vb." In these cases, the correlation equals 1, and it represents a high correlation between vehicle-pedestrian collisions and the presence of pedestrians in accidents, given that pedestrians are involved in all vehicle-pedestrian collisions.…”

“…Many different studies discuss dangerous road infrastructure problems [13][14][15], which include the influence of traffic lights, road barriers, and signs on increasing crash severity. However, for a more comprehensive analysis, studies by Billah et al [13], Chen et al [14], and others examined the influence of other factors on crash severity, for example, lighting [14][15][16] and weather conditions [15,16], human factors [14][15][16][17][18], and vehicle characteristics [12,13,[19][20][21][22][23][24][25][26]. Azhar et al [20], Pillajo-Quijia et al [27], and Chen et al [14], Refs.…”

“…However, for a more comprehensive analysis, studies by Billah et al [13], Chen et al [14], and others examined the influence of other factors on crash severity, for example, lighting [14][15][16] and weather conditions [15,16], human factors [14][15][16][17][18], and vehicle characteristics [12,13,[19][20][21][22][23][24][25][26]. Azhar et al [20], Pillajo-Quijia et al [27], and Chen et al [14], Refs. [28][29][30][31][32][33][34] highlighted the influence of night time on increasing crash severity.…”

“…Onieva-García et al [18] and Chen et al [14] proved that the gender and age of road users are factors in the severity of crashes. Wang et al [19], Billah et al [13], and Azhar et al [20] considered how different types of road accidents can influence fatality levels. As a result of these studies, rollover and run-off-roadway accidents have the highest impact.…”

Classifying the Severity Levels of Traffic Accidents Using Decision Trees

“…Till date, the number of studies on applying decision trees to traffic accident data in Malaysia is still very limited. Rusli et al (2018) focused on the accidents along rural mountainous highway, while Azhar et al (2022) emphasize on the accidents involving heavy vehicle drivers. Hence, this paper aims to fill the gap through the construction of decision trees for the the set of accident data in Malaysia.…”