An Explainable Spatial Analytics and Machine Learning Framework for Highway–Rail Grade Crossing Safety Assessment

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas.