Evaluating transit network resilience through graph theory and demand-elastic measures: Case study of the Toronto transit system
Bibliographic record
Abstract
The reliability of public transit networks is of critical importance the world over. As many transit systems are increasingly exposed to various causes of service disruptions, there exists a need to quantitatively measure the operational resilience of a transit network. This paper presents an approach for transit resilience measurement that combines several metrics from the existing literature. As a case study, the paper examines and quantifies the resilience of the public transit network in Toronto, Canada to operational disruptions. The approach adopted in this work is a combination of quantitative methods founded in Graph Theory, where the public transit network is represented as a directional graph, and demand-elastic methods using transportation network simulation models to complement the network science approaches. 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 reasons for their inherent critical nature are discussed and analyzed. This work was also able to spatially quantify transit resilience by identifying low-risk and at-risk areas within Toronto. Although the results are specific to Toronto, making it the first study to analyze transit resilience elaborately in this city, the techniques employed can be applied to any sufficiently detailed transit network.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".