Driver Injury Prediction and Factor Analysis in Passenger Vehicle-to-Passenger Vehicle Collision Accidents Using Explainable Machine Learning

Vehicle accidents, particularly passenger vehicle-to-passenger vehicle (PV-PV) collisions, cause significant property damage and driver injuries, resulting in substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits precise severity prediction. This study investigates various accident-related factors, including environmental conditions, vehicle attributes, driver characteristics, pre-crash scenarios, and collision dynamics.Using data from the National Highway Traffic Safety Administration’s (NHTSA) Crash Report Sampling System (CRSS) and Fatality Analysis Reporting System (FARS), the dataset was balanced by integrating complementary severity-level data with random over-sampling and under-sampling techniques. The Minimum Redundancy Maximum Relevance (mRMR) algorithm was used for feature selection to minimize redundancy and identify key features.Five advanced machine learning models were employed for severity prediction, with Extreme Gradient Boosting (XGBoost) achieving the best performance: 84.9% accuracy, 84.85% precision, 84.90% recall, and an F1-score of 84.87%. SHapley Additive exPlanations (SHAP) analysis was employed to interpret the model and conduct a deep analysis of accident features, including feature importance, dependencies, and their combined effects on severity prediction.The findings highlight the effectiveness of interpretable machine learning in understanding accident severity and identifying key factors influencing driver injuries.