Performance Metrics and Analysis of Transit Network Resilience in Toronto
Bibliographic record
Abstract
Slow expansion and inadequate upgrades of the transit network in Toronto, combined with unprecedented levels of demand, have created a daily commute for transit users which is plagued by delays and disruptions. This paper aims at examining and quantifying the resilience of the public transit network in Toronto to operational disruptions. It also attempts to identify critical points within the network, as well as the spatial impact of service disruptions. The study of resilience is a relatively new research topic, with a limited breadth of research conducted to date, especially when one considers the resilience of a public transit network. This paper intends to fill this research gap by proposing a new framework for resilience measurement and analysis. The approach adopted in this work is a unique combination of quantitative methods founded in Graph Theory and demand-elastic methods of transportation network analysis using EMME4. The research findings revealed the critical stations in Toronto’s subway network, which if disrupted, would create major negative impacts on passenger trip times. The underlying reasons for their inherent critical nature is discussed and analyzed. In addition, this work was able to spatially quantify transit resilience by identifying both low-risk and at-risk areas within Toronto.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".