Advancements in Machine Learning for Traffic Accident Severity Prediction: A Comprehensive Review

Abstract

This literature review categorizes machine learning studies in traffic accident severity prediction, providing a comprehensive overview of the diverse applications and advancements in this field. It begins with a comparative analysis of machine learning models, highlighting the performance of various algorithms such as Random Forest, XGBoost, and Support Vector Machines (SVM) in predicting accident severity. The review also explores factor-specific studies, emphasizing the influence of road, environmental, and vehicle-related factors on crash outcomes. These studies demonstrate the critical role of factors such as road type, weather conditions, and vehicle characteristics in determining accident severity. Additionally, crash-type-specific prediction models have been developed, showcasing the ability of machine learning models to tailor predictions based on the nature of the crash, whether involving pedestrians, vehicles, or specific collision types. The review also examines hybrid and ensemble approaches, which combine multiple algorithms to enhance prediction accuracy. These approaches leverage the strengths of individual models to improve overall performance, offering a promising direction for future research. By categorizing the studies into these key areas, this review provides a structured understanding of the state-of-the-art in machine learning applications for traffic accident severity prediction and identifies opportunities for further development to enhance prediction robustness, accuracy, and applicability in real-world traffic safety management.