Risk propagation mechanisms in railway systems under extreme weather: A knowledge graph-based unsupervised causation chain approach

Frequent and intensive adverse weathers can cause severe rail accidents through domino effect, posing significant challenges to railway safety and operational reliability. A detailed elucidation of the risk propagation mechanism across hazardous events is critical for effective risk management in rail transportation. Risk pathways involve various meteorological factors, infrastructure vulnerabilities, and consequences, in which each exhibits distinct causation strengths, trigger probabilities, severity levels, and high-impact points. To disclose the characteristics of weather-related railway domino effect accidents, this paper develops a novel railway causation analysis methodology based on an event logic graph. This framework enhances existing knowledge graph-based methodologies by emphasizing the evolution and logical progression of sequential hazardous events. Besides, an unsupervised accident causation chain linking technique is proposed, which integrates historical accident data into the knowledge graph to build a comprehensive graph database. It facilitates data-driven analysis of both structured and unstructured accident records without requiring laborious annotations. By thoroughly evaluating topological features and statistical indicators via a real-world dataset of weather-related railway accidents, key risk propagation patterns such as risk path dependence, path convergence, and risk escalation curves are recognized. Critical nodes including risk amplifiers, critical junctures, and marginal risk contributors within six critical domino chains are identified. These findings inform targeted risk mitigation strategies to prevent risk propagation and escalation. The proposed methodology and results offer theoretical support and actionable insights for enhancing safety and reliability management of railway systems under extreme weather conditions.