Social Equity and Bus On-Time Performance in Canada’s Largest City
Bibliographic record
Abstract
Bus routes provide critical lifelines to disadvantaged travelers in major cities. Bus route performance is also more variable than the performance of other, grade-separated transit modes. Yet the social equity of bus operational performance is largely unexamined outside of limited statutory applications. Equity assessment methods for transit operations are similarly underdeveloped relative to equity analysis methods deployed in transit planning. This study examines the equity of bus on-time performance (OTP) in Toronto, Ontario, the largest city in Canada. Both census proximity and ridership profile approaches to defining equity routes are deployed, modifying United States Department of Transportation (U.S. DOT) Title VI methods to fit a Canadian context. Bus OTP in Toronto is found to be horizontally equitable. It is also found that the U.S. DOT approach of averaging performance between equity and non-equity routes masks the existence of underperforming routes with very significant ridership of color. These routes are overwhelmingly night routes, most of which are only classified as equity routes using a ridership definition. These results suggest that the underperformance of Toronto’s “Blue Night” network of overnight buses is a social equity issue. This OTP data is also applied to a household travel survey to identify disparities in the OTP of bus transit as experienced by different demographic groups throughout the city. It is found that recent immigrants and carless households, both heavily transit dependent populations in the Canadian context, experience lower on-time bus performance than other groups.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".