Beyond traditional metrics: Redefining urban metro network vulnerability with redundancy assessment
Bibliographic record
Abstract
Previous studies have predominantly analyzed Urban Metro Network (UMN) vulnerability from topological and functional perspectives, often neglecting the impact of disruptions on alternative route availability. This research introduces a novel redundancy-based vulnerability analysis, assessing the reduction in travel alternatives following disruptions. The Montreal UMN is used as a case study , utilizing General Transit Feed Specification (GTFS) data from the Montreal Transit Authority and trip data from the 2018 Montreal Origin-Destination survey. Using the open-source platform Transition, we simulate shortest transit routes for each trip, generate alternative routes, and compute travel times. We define one targeted and three random failure scenarios, selected from 100 simulations, to evaluate network vulnerability to various disruption types. Indicators are formulated, calculated, and compared across all scenarios. Each failure scenario involves a sequence of consecutive metro station disruptions, leading to complete network shutdown. Findings reveal that the metro network is significantly more vulnerable to targeted disruptions than random ones. Among all indicators, functional ones related to users' travel time show greater sensitivity to disruption type, be it targeted or random. Vulnerability indicators exhibit the most substantial changes during initial disruptions, highlighting their critical impact. Although traditional approaches (topological and functional) show a direct relationship between the number of disruptions and changes in vulnerability indicators, this is not true for the redundancy-based vulnerability indicator. In this case, the primary determinants are the locations of disrupted stations and the network's geometry, rather than the number of disruptions.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".