Factors Affecting Crash Involvement of Commercial Vehicle Drivers: Evaluation of Commercial Vehicle Drivers’ Characteristics in South Korea

Jooyoung Lee,Jiho Yeo,Sanghyeok Kang,Ilsoo Yun

doi:10.1155/2020/5868379

Abstract

The aim of this study was to evaluate the effects of driver-related factors on crash involvement of four different types of commercial vehicles—express buses, local buses, taxis, and trucks—and to compare outcomes across types. Previous studies on commercial vehicle crashes have generally been focused on a single type of commercial vehicle; however, the characteristics of drivers as factors affecting crashes vary widely across types of commercial vehicles as well as across study sites. This underscores the need for comparative analysis between different types of commercial vehicles that operate in similar environments. Toward these ends, we analyzed 627,594 commercial vehicle driver records in South Korea using a mixed logit model able to address unobserved heterogeneity in crash-related data. The estimated outcomes showed that driver-related factors have common effects on crash involvement: greater experience had a positive effect (diminished driver crash involvement), while traffic violations, job change, and previous crash involvement had negative effects. However, the magnitude of the effects and heterogeneity varied across different types of commercial vehicles. The findings support the contention that the safety management policy of commercial drivers needs to be set differently according to the vehicle type. Furthermore, the variables in this study can be used as promising predictors to quantify potential crash involvement of commercial vehicles. Using these variables, it is possible to proactively identify groups of accident-prone commercial vehicle drivers and to implement effective measures to reduce their involvement in crashes.

Highlights

Commercial vehicles have a high risk of traffic injury because they are driven long distances, which often leads to driver fatigue [1]
A mixed logit model was used to unveil significant factors that affect the crash involvement of commercial vehicle drivers in South Korea. e mixed logit model assumes that the effects of the parameters on the logit model are different for each individual [31]. us, the model can account for unobserved factors that are not captured directly through the data
Results and Discussion e results of the mixed logit model derived in this study successfully converged for all vehicle types, and the variables with random parameters showed statistically significant results

Summary

Introduction

Commercial vehicles have a high risk of traffic injury because they are driven long distances, which often leads to driver fatigue [1]. When involved in traffic crashes, the heavy weight of such vehicles generates greater impact damage on the occupants of other vehicles or pedestrians [2, 3]. 6.2% of total registered vehicles in South Korea, the proportions of traffic injuries and deaths involving commercial vehicles are substantially higher than those of other vehicle classes. While controlling for driving distance, commercial vehicles had 792.7 traffic accidents and 12.6 traffic deaths per one billion km, both of these values were 1.5 times greater than the values for noncommercial vehicles. A large portion of traffic crashes is caused by driver-related factors [5]

Objectives

Methods

Results