Enhancing Intelligent Transportation Safety with Explainable AI: A Framework for Uncovering Crash Severity Factors at Highway–Rail Grade Crossings

Abstract

Improving road safety is a fundamental goal of Intelligent Transportation Systems (ITS). However, the complex interplay of factors in accident-prone scenarios, such as highway–rail grade crossings, poses significant challenges for conventional analysis. This paper addresses this gap by proposing and validating a novel explainable artificial intelligence (XAI) framework, which integrates Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanations (SHAP), to enhance safety analysis within ITS. Applying this framework to a comprehensive dataset of highway–rail grade crossing collisions, our research moves beyond simple correlation to uncover the nonlinear relationships and interaction effects governing injury severity. The model identifies speed-related factors, driver age, and traffic exposure as primary predictors. More critically, the SHAP analysis quantitatively reveals significant synergistic risks, demonstrating that the combination of non-dry road surfaces and poor lighting conditions drastically amplifies injury severity. These findings offer granular insights for the “smart management” and development of “resilient infrastructures,” enabling targeted interventions like adaptive lighting systems and dynamic risk warnings. This study not only provides critical safety solutions for grade crossings but also showcases the power of XAI as a robust tool for “advanced analysis” across various complex transportation safety problems, ultimately contributing to the creation of safer and more reliable ITS.