A New Failure Strategy to Enhance the Vulnerability Assessment of Urban Metro Networks
Bibliographic record
Abstract
Urban Metro Networks (UMNs), as an integral component of transit systems, play a crucial role in the overall efficiency of urban transportation. Disruptions in their operations can significantly impact daily travel. Consequently, numerous studies have focused on assessing the vulnerability of UMNs. However, many of these studies have not adequately considered the infrastructural characteristics of UMNs. This study seeks to emphasize the importance of incorporating infrastructural features—particularly Stations with Direction-Change Facilities (SDCFs)—to enhance the accuracy of research findings. To address this, we introduce the Block-Based Failure Strategy (BBFS), a novel approach that accounts for SDCFs. The Montreal UMN is selected as the case study, and its vulnerability to targeted disruptions is assessed using both BBFS and a conventional strategy known as the Node-Based Failure Strategy (NBFS). The results reveal a significant disparity between simulations conducted using BBFS and NBFS. When SDCFs are considered and BBFS is applied, the network is deemed fully degraded after 9 targeted disruptions. In contrast, the NBFS simulation suggests that the network can withstand up to 35 targeted disruptions before becoming completely degraded—a figure much higher than what would realistically occur.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".