Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Thanapong Champahom,Vuttichai Chatpattananan,Ampol Karoonsoontawong,Vatanavongs Ratanavaraha,Sajjakaj Jomnonkwao

doi:10.1155/2019/2568978

Abstract

Objective. Among crash types on Thai highways, rear-end crashes have been found to cause the largest number of fatalities. This study aims to find ways to decrease rear-end crashes and fatal rear-end crashes. Methods. Classification and regression tree (CART) was used to analyze the complicated relationship of variables of big data. The analysis was conducted by creating two models: (1) a model which indicates the causes of rear-end crashes by applying Quasi-Induced Exposure to at-fault driver characteristics; (2) a determined model which studies fatal crashes. Results. Predictor variables in the model of at-fault and not-at-fault drivers found that driver age is most significant, followed by number of lanes and median opening area. For the mode of fatality, the use of safety equipment was found to be of most importance. Conclusion. The model results can be used to develop guidelines for public awareness programs for motorists and to propose policy changes to the Department of Highway in order to reduce the severity of rear-end crashes. Moreover, this paper discusses the variables that may result in both the perspective of rear-end crash number and the fatality rate of rear-end crashes as strategies in future research.

Highlights

Crash trends on ailand highways are continuously on the increase [1,2,3]
A study of the causes of rear-end crashes, focusing on at-fault and not-at-fault drivers, has found that most crashes are caused by drivers not leaving enough space between their own car and cars in front [4]. erefore, the cause of rear-end collisions, is the car behind [5]. is study focuses on the driver characteristics of the at-fault driver, that is, the driver of the car behind that crashes into the car in front, by applying Quasi-Induced Exposure Methods [6]
Ese methods have been widely used in the eld of tra c accident research. e principle of these methods is to predict the at-fault driver based on the accident report [7, 8] by supposing that the distribution of not-at-fault drivers closely represents the distribution of exposure to accident hazards [9, 10]

Summary

Introduction

Crash trends on ailand highways are continuously on the increase [1,2,3]. Crash type statistics reveal that rear-end collision is the second most common type of collision. The highest number of fatalities occur as a result of rear-end collisions (Figure 1). A study of the causes of rear-end crashes, focusing on at-fault and not-at-fault drivers, has found that most crashes are caused by drivers not leaving enough space between their own car and cars in front [4]. Erefore, the cause of rear-end collisions, is the car behind [5]. Is study focuses on the driver characteristics of the at-fault driver, that is, the driver of the car behind that crashes into the car in front, by applying Quasi-Induced Exposure Methods [6]. This research explores the relevance of the high fatality rate caused by rear-end crashes. Fatal crashes must be considered from a characteristic study of rear-end crashes by focusing on ways to reduce fatalities. Et al [12] found that the greater the di erence in velocity of the struck car and the striking car, the higher the number of Number of crash and fatal crash

Methods

Results

Conclusion