Analysis of Cascading Failure in Urban Metro Networks: A Dynamic Perspective Incorporating Changes in Travel Decisions
Bibliographic record
Abstract
Accidents in urban subway networks often exert substantial and enduring impacts on urban transportation, drawing widespread attention from researchers. However, prevailing studies predominantly employ static methodologies to scrutinize incidents in urban subway systems, frequently assuming constancy in network topology and individual travel plans. Nonetheless, alterations in subway passenger flow also wield influence over network resilience. The manifold fluctuations in post‐accident traffic flow and their repercussions on elastic transportation demand have hitherto been inadequately explored in the extant literature. Moreover, existing cascading failure models often lack precise definitions of scenarios, as in actual scenarios, passenger overflow following station closures can lead to cascading failures in subway networks. To bridge this gap, this study meticulously defines the research scenario, demarcates potential post‐accident travel decisions, and analyzes the multifaceted factors influencing cascading failures in urban subway networks. Specifically, the proposed cascading failure model takes into consideration both the static characteristics of network topology and the fluctuations in passenger volume, along with their interplay. Additionally, when assessing station importance and quantifying network resilience, multiple travel decisions under different fault durations are duly considered. The study’s validation is conducted within the urban subway network of Nanjing, China. The findings suggest that accidents and fault stations of varied durations exert disparate impacts on passenger travel decisions, thereby influencing subway resilience. Notably, stations with integrated functionalities may hold more significant roles than those with singular functions. Furthermore, our results indicate a V‐shaped effect concerning event duration—as the duration increases, the proportion of passengers canceling subway travel rises, thereby enhancing the likelihood of stabilizing the subway network. Consequently, this study, by incorporating realistic variations in travel decisions, furnishes a comprehensive understanding of the impact of events on urban subway networks. This dynamic perspective yields valuable insights for optimizing strategies in responding to events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".