Risk evaluation of urban rail transit emergencies using a multilayer hypernetwork approach

With the rapid expansion of Urban Rail Transit (URT) systems and the increasing complexity of their operating environments, emergency events are becoming more frequent and diverse and are characterized by chain propagation, which poses serious risks to transportation safety. Traditional complex network approaches often fail to accurately represent the intricate interactions among various multidimensional and heterogeneous factors. To address this gap, this study develops a three-layer hypernetwork model integrating location, cause, and consequence nodes, based on emergency data from the Beijing Metro spanning 2013–2023. A comprehensive risk evaluation framework is established by integrating network metrics such as node super-degrees and clustering coefficients with gravitational models, resistance distances and interdependence coefficients. Empirical analysis reveals that a few hub stations and key causes in the rail transit network play a leading role in the spread of accidents. Several high-risk propagation chains are identified, and tailored risk control strategies are proposed. This study provides theoretical support and practical insights for enhancing emergency preparedness and operational resilience in URT systems.