Assessing and mitigating traffic crash risks using interpretable machine learning techniques

This study aims to examine factors associated with self-reported crash involvement among drivers in Pakistan using interpretable machine learning (ML) techniques and established driver-behavior instruments. The data used in this study were collected through an Internet-based survey in Pakistan utilizing a self-report questionnaire (cross-sectional design; N:623 drivers). The questionnaire consisted of items related to dangerous (aggressive, risky, and negative emotions), aberrant (errors and violations), and positive driving behaviors, along with demographics and items related to self-reported crashes (Yes/No) over the past three years. Interpretable ML techniques, including Logistic Regression, Categorical Boosting (CatBoost), and eXtreme Gradient Boosting (XGBoost) were employed. Furthermore, to improve the interpretation of ML techniques for self-reported crash risk factors, the Shapley Additive explanation (SHAP) technique was used. The results revealed that aggressive driving and risky driving behaviors, errors, and violations committed while driving, were associated with increased traffic crashes. Prior motorcycle riding experience variable also had a significant association with the occurrence of traffic crashes. Moreover, XGBoost outperformed the CatBoost and Logistic Regression models in terms of predictive performance. XGBoost achieved the highest test accuracy (0.86), compared to CatBoost (0.80) and Logistic Regression (0.73). The study underscores the importance of integrating interpretable ML in traffic safety research. The findings suggest that behavior-oriented road safety interventions such as aggressive and risky driving enforcement, driver education, and risk perception should be prioritized and enhanced. Prior motorcycle-riding experience is important, transition-oriented training should be designed for riders to adapt vehicle characteristics and road usage patterns.