Studying Traffic Accidents Utilizing a Machine Learning Approach: Case Study of Jordan

Abstract

Traffic accidents continue to be a significant public safety concern in Jordan, largely due to the many interaction of human, vehicle, road, and environmental factors. The research presented here is based on a data-driven analysis of fatal traffic crashes in Jordan with a comprehensive dataset using a national dataset covering all recorded crashes between 2018 and 2022. Within this dataset, there is detailed information about driver demographics, vehicle characteristics, conditions of the road, environmental factors and accident attributes, providing the necessary information to perform a complete machine learning analysis. Statistical analyses were conducted using the selected features to identify and quantify the relationships most strongly associated with death resulting from traffic accidents. Machine learning models were developed to predict whether an accident would result in a fatal outcome. The most accurate machine learning models were Random Forest and XGBoost, both of which achieved an accuracy of approximately 0.96 overall, while further evaluation using class-sensitive metrics highlighted differences in their ability to identify fatal cases. To improve interpretability, explainable artificial intelligence techniques were integrated into the analysis. SHAP was used to identify the most influential factors helping in fatal incidents outcomes at a global phase, while LIME gave localized explanations for the prediction of individual. The added value of combining machine learning with explainable models to better understand the mechanisms underlying fatal traffic accidents was of great focus on this work. The results support evidence-based policy interventions within Jordan, including targeted enforcement measures, infrastructure improvements, and strategies addressing behaviors that are related to high-risk driving in order to reduce fatalities and serious injuries resulting from traffic accidents.