Functional Resilience Assessment of Urban Rail Transit Stations from a Network Perspective--A Case Study of Shenzhen

This study explores challenges in maintaining operational resilience for urban rail transit networks during emergencies. It develops a comprehensive evaluation system based on resilience triangle theory, covering pre-event, during-event, and post-event stages to assess station functional resilience across the full event cycle. By integrating complex network theory with dynamic passenger flow simulation, the research analyzes transportation system topological features and passenger adaptive behaviors during incidents. Key methodological innovations include a dual-constraint feasible path criterion that combines time flexibility thresholds and transfer tolerance parameters to evaluate passenger preferences for efficiency and comfort simultaneously, and a spatiotemporally extended recovery efficiency model using time-varying passenger load factors to overcome limitations of traditional static assessments. Empirical analysis of Shenzhen’s rail transit network verifies the framework’s ability to accurately identify and quantify the resilience performance of critical vulnerable nodes. Findings reveal terminal stations like Buji and Mucunwan reduce the largest connected subnetwork ratio to approximately 90%, significantly degrading overall network connectivity; over 85% of passengers at stations within Futian District’s central loop can switch routes, indicating high structural redundancy; and during morning peak hours, stations like Wuhe and Minzhi exhibit recovery efficiencies below 1% with recovery times exceeding 120 minutes, demonstrating substantial spatiotemporal resilience differences. The research provides a quantifiable, stage-specific, and behavior-integrated evaluation tool for rail transit resilience assessment, offering theoretical and practical insights for enhancing resilience evaluation systems through network-passenger interaction analysis.