Comparison of road crash severity prediction using tree-based machine learning algorithms with varying data treatments

Abstract

The upsurge in road crashes is a global challenge, and it poses a serious threat especially in low-and-middle-income countries due to incessant road crashes claiming many lives. Road safety management focuses on mitigating crashes by predicting the frequency and severity of the crashes and building safer roads. Predicting crash severity is essential because it is always preferred to avoid severe crashes. Factors like the time of the crash, type of collision, type, and number of vehicles involved in the crash, and the road features like geometric properties, pavement condition, and surrounding environment of the crash location can govern the severity of crashes. Due to the involvement of numerous factors in crash occurrence, predicting crash severity with high accuracy is a difficult task. Tree-based classification models like decision trees and random forests are two types of machine learning algorithms widely used to predict crash severities because they are considered to produce accurate predictions. The objective of this study is to develop road crash severity prediction models for a mountainous highway using the two tree-based algorithms and to assess various factors that affect the severity of the crashes by taking into account two different data treatment approaches: a) only considering the type of vehicle involved in a crash, and b) considering both the type and number of vehicles involved in a crash. The performance of the two treebased models: decision tree and random forest models for two separate types of data treatment will be assessed and compared. The models can be used to predict crash severity and the results may be useful for road agencies to identify and select safety countermeasures that can contribute to lower crash severity.