Abstract
Motorcycle crash severity is under-researched in Ghana. Thus, the probable risk factors and association between these factors and motorcycle crash severity outcomes is not known. Traditional statistical models have intrinsic assumptions and pre-defined correlations that, if flouted, can generate inaccurate results. In this study, machine learning based algorithms were employed to predict and classify motorcycle crash severity. Machine learning based techniques are non-parametric models without the presumption of relationships between endogenous and exogenous variables. The main aim of this research is to evaluate and compare different approaches to modeling motorcycle crash severity as well as investigating the effect of risk factors on the injury outcomes of motorcycle crashes. Motorcycle crash dataset between 2011 and 2015 was extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. The dataset was classified into four injury severity categories: fatal, hospitalized, injured, and damage-only. Three machine learning based models were developed: J48 Decision Tree Classifier, Random Forest (RF) and Instance-Based learning with parameter k (IBk) were employed to model the severity of injury in a motorcycle crash. These machine learning algorithms were validated using 10-fold cross-validation technique. The three machine learning based algorithms were compared with one another and the statistical model: multinomial logit model (MNLM). Also, the relative importance analysis of the attribute was conducted to determine the impact of these attributes on injury severity outcomes. The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability. Location type, time of the crash, settlement type, collision partner, collision type, road separation, road surface type, the day of the week, and road shoulder condition were found as the critical determinants of motorcycle crash injury severity.
Highlights
Injuries resulting from road traffic crashes are a significant cause of death and disability with a disproportionate number occurring in African countries
The dataset is loaded as an Attribute-Relation File Format (ARFF) file into the Waikato Environment for Knowledge Analysis (WEKA) data mining tool
Machine learning methods are non-parametric techniques that have been widely used in transportation research but are Prediction of motorcycle crash severity by machine learning still relatively underutilized in motorcycle crash severity analysis
Summary
Injuries resulting from road traffic crashes are a significant cause of death and disability with a disproportionate number occurring in African countries. Motorcycles are eco-friendly, offer a flexible, convenient, and inexpensive means of transportation when compared with four wheelers automobiles with an internal combustion engine. Regardless of these merits of motorcycles, there is a rising in safety concerns about the usage of motorcycles [6]. Motorcycle crashes regularly occur on shared highways where motorcyclists take unusual and perilously riding behaviors These riding behaviors include aggressive diverging, over speeding, riding in wrong-direction, unlawful lane changing, and wrong overtaking. These perilously riding behaviors of motorcyclists can lead to increasing the chance and level of severity of motorcycles involved in road traffic crashes [10]
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