Abstract:
Traffic accidents are becoming a global issue, causing enormous losses in both human and
financial resources. According to a World Health Organization assessment, the severity of
road accidents affects between 20 and 50 million people each year. This study intends to
examine significant factors that contribute to road traffic accident severity. Seven machine
learning models namely, Naive Bayes, KNN, Logistic model tree, Decision Tree, Random
Tree, and Logistic Regression machine learning models were compared to the J48 pruned
tree model to analyze and predict accident severity in the road traffic accident. To compare
the effectiveness of the machine learning models, ten well-known performance evaluation
metrics were employed. According to the experimental results, the J48 pruned tree model
performed more accurately than the other seven machine learning models. According to
the analysis, the number of casualties, the number of vehicles involved in the accident, the
weather conditions, and the lighting conditions of the road, is the main determinant of road
traffic accident severity.
