Vulnerability Analysis of Urban Rail Transit Network considering Cascading Failure Evolution
Bibliographic record
Abstract
Vulnerability analysis is the premise of operational risk management and control for the large-scale and complex urban rail transit network (URTN) under the operation interruption of important stations. The temporary operation interruption of one station in an emergency may lead to the cascading failure and the paralysis of the whole URTN due to the load of other stations exceeding the limited capacity. The priority of important stations is proposed by combining its location and function in URTN. In addition, focusing on the analysis of the travel behaviour of passengers and the synergy of public transport networks, a novel cascading failure evolution model is established to simulate the cascading failure process of URTN under different attack scenarios. The vulnerability indicators are constructed to dynamically evaluate the vulnerability of URTN considering cascading failure evolution, which are different from the traditional vulnerability indicators based on complex network theory. Taking the Beijing urban rail transit network as an example, the dynamic simulation results show that the cascading failure of URTN is closely related to the temporal-spatial distribution of passenger flows and malicious attacks are more destructive than random attacks. Compared with the important stations with the largest betweenness or degree, the interrupted stations with largest intensity have a greater impact on the operational stability of URTN. Moreover, increasing the capacity coefficient of the station can reduce the vulnerability of URTN.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".