Thailand’s intercity railway system is dominated by single-track operations, where limited passing loops, priority rules and aging infrastructure make services highly vulnerable to delay propagation. Existing analytical and simulation-based methods struggle to capture complex, non-linear interactions between operational factors, infrastructure constraints, and real-time information. This study introduces a real-time delay prediction framework specifically designed for single-track intercity railways, integrating both scheduled features and evolving en-route conditions. A cross-validated random forest model, enhanced with explainable SHAP analysis, is developed to generate real-time predictions of arrival delays at destination stations. The model demonstrates strong predictive accuracy (R² = 0.84; MAE = 2.53 min), with 87.5% of forecasts fall within 5 min of actual arrival delay and 94.9% fall within 10 min, confirming its suitability for operational decision support. SHAP interpretation reveals that real-time delay propagation variables dominate prediction outcomes, while service characteristics and infrastructure factors contribute secondary but meaningful effects. The proposed framework provides practical, real-time value for dispatchers, enabling proactive routing decisions, congestion mitigation, and improved passenger communication. This work offers one of the first explainable machine-learning delay prediction models tailored to single-track railway operations and presents insights applicable to similarly constrained rail systems worldwide.
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