Innovative prediction and causal analysis of accident vehicle towing probability using advanced gradient boosting techniques on extensive road traffic scene data

Abstract

Accurate prediction and causal analysis of road crashes are crucial for improving road safety. One critical indicator of road crash severity is whether the involved vehicles require towing. Despite its importance, limited research has utilized this factor for predicting vehicle towing probability and analyzing its causal factors. This study addresses this gap by predicting the probability of vehicle towing in road crashes based on road scene features and identifying key causal factors. Utilizing the Transportation Injury Mapping System (TIMS) dataset from California, USA, encompassing 12 years, 14 relevant features, and over 2 million road crash records, research team developed a prediction model using advanced gradient boosting techniques. Our model outperforms Random Forest, GBDT, and XGBoost in predictive accuracy. Employing the Shapley Additive Explanation (SHAP) method, researchers elucidate seven key factors influencing towing necessity. These findings introduce a novel predictive approach and offer valuable insights for road crash risk assessment and road safety planning.