Adeliyi, Timothy T.Oluwadele, DeborahIgwe, KevinAroba, Oluwasegun Julius2023-08-032023-08-032023-06-30Adeliyi, T.T. et al. 2023. Analysis of road traffic accidents severity using a pruned tree-based model. International Journal of Transport Development and Integration. 7(2): 131-138. doi:10.18280/ijtdi.0702082058-83052058-8313 (Online)https://hdl.handle.net/10321/4929Traffic 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.8 penAccident severityMachine learning algorithmsPruned tree-based modelRoad traffic accidentArticle2023-07-2510.18280/ijtdi.070208