A Social Equity Lens on Toronto Transit Network Performance using a Graph Theory Approach: Examining Criticality, Service Redundancy, Transit Delays and Disruptions
Bibliographic record
Abstract
Public transit delays and disruptions are inevitable occurrences in many transit sys- tems. Past studies did not differentiate between different groups of riders when study- ing the disruption impacts and rarely integrated service frequency into their network models. This study adds a social equity layer by determining if different equity- seeking groups were more vulnerable to disruptions or had less resiliency than the general population.Various graph theory measures were used in the analysis, and both a time-expanded and an L-Space or route-map representations of the Toronto transit network were adopted. Census and travel demand survey data were used to determine trip patterns for the equity analysis. The results show that equity-seeking riders had slightly greater vulnerability and less redundancy in hypothetical scenarios of service disruptions compared to the gen- eral population. However, when analyzing real-world disruptions that occurred in 2019-2020, equity seeking riders were more resilient compared to the general population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".