Quantifying System-Level Risk at Highway–Rail Grade Crossings: Integrating Spatial Autocorrelation and Explainable Machine Learning

Highway–rail grade crossing (HRGC) safety analysis is often based on raw incident counts or site-level models that do not adequately control for regional crossing-density exposure and frequently ignore spatial dependence. This limits the ability to identify where risk is structurally concentrated across the rail network. The problem is important because misidentifying high-risk environments leads to inefficient allocation of limited safety resources and weakens corridor-level intervention strategies. This study introduces accumulated incidents per crossing (AIPX), a crossing-count-normalized cumulative incident intensity metric that measured cumulative incident burden at the county level over a 51-year period (1975–2025). The study developed an algorithmic framework that integrates data reconciliation with spatial autocorrelation analysis, distributional modeling, and nonparametric machine learning to identify and interpret high-intensity risk environments. Global Moran’s I indicates statistically significant positive spatial autocorrelation (I = 0.359, p = 0.001), suggesting that incident intensity is spatially clustered rather than random. Local indicators identify statistically significant high and low intensity county clusters. Distributional analysis shows that AIPX within high-intensity clusters was best represented by lognormal and Johnson SU distributions. Machine learning models achieved strong classification performance (AUC ≈ 0.85). Explainability methods consistently identified temperature, train direction, crossing warning configuration, train composition, and track class as dominant associated features. These variables function as proxies for broader geographic, operational, exposure, and network-structure differences rather than direct causal drivers. The findings indicate a pattern consistent with regional and network-level exposure regimes concentrated along freight-intensive corridors. The study provides a transparent analytical workflow that supports corridor-level prioritization of safety interventions and more effective allocation of infrastructure investments.