Identification Method of Critical Stations in Urban Rail Transit Networks Considering Turnback Intervals

Identifying critical stations is fundamental to improving the resilience and operational safety of urban rail transit networks. However, most existing identification methods—especially dynamic node removal approaches—assume that station failures affect only the failed node itself, thereby overlooking the cascading impacts caused by train turnback adjustments under bidirectional service interruptions. This simplification leads to systematic underestimation of stations with strong operational dependencies. To address this gap, this study proposes a framework for identifying critical station that explicitly incorporates bidirectional operational disruptions and the indirect failures they induce within turnback sections. This study is among the first to explicitly model turnback-related failure propagation within operational sections in critical station identification, providing a closer alignment with real-world rail transit operations. A comprehensive evaluation system is then constructed by integrating dynamic network connectivity indicators, network topology characteristics, and station attributes. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), combined with objectively determined indicator weights, is employed to synthesize multidimensional indicators and rank station importance. The method is applied to the Chengdu Metro network (12 lines and 282 stations). Results indicate that considering turnback related indirect failures substantially amplifies the measured impact of station disruptions on network connectivity. Critical stations are highly concentrated at intersections between the loop line and major radial lines, while several non-interchange stations within key turnback sections—such as Lijiatuo Station and Wannianchang Station—exhibit pronounced increases in importance rankings. Comparative analysis shows that the rankings of some stations change by more than 50% relative to the conventional node removal method, indicating that traditional approaches may significantly underestimate operationally critical stations associated with turnback sections. More importantly, the proposed method enables a direct comparison between structurally important stations and operationally critical stations under disruption scenarios. Overall, the proposed framework provides a more realistic and operation oriented identification of critical stations by explicitly accounting for train operation dependencies under bidirectional interruptions, offering practical insights for resilience assessment and emergency management of large scale urban rail transit networks.