Analyzing crash severity through impairment and protection: A hybrid XGBoost-Bayesian network approach

This study examines the interplay among protection status, impairment, driver demographics, vehicle type, and road characteristics, providing a deeper understanding of how these factors collectively influence crash severity. In addition to analyzing these complex relationships, the study introduces a probabilistic and robust framework that moves beyond traditional regression models. By integrating XGBoost for feature selection and Bayesian networks for conditional probability modeling, this approach captures conditional dependencies among key factors, enhancing interpretability. In addition to scenario-based relative risk estimation, the study introduces a temporal component by comparing pre-COVID and post-COVID crash patterns. The findings support data-driven, targeted interventions and policies, offering a flexible and interpretable tool for monitoring crash severity trends and guiding effective safety strategies over time.