Identifying clusters of public transit unreliability through an equity lens using GIS: A study of Winnipeg, Manitoba, Canada
Bibliographic record
Abstract
This thesis investigates the clusters of public transit service unreliability using GIS techniques through an equity lens. In the first study, I analyzed transit on-time performance and pass-up records in the city of Winnipeg, Manitoba, Canada, using spatial scan statistics and identified clusters of high- and low-risk areas for unreliable transit services such as delays and early arrivals. I also discovered that high-risk clusters are associated with socio-economically disadvantaged neighbourhoods, suggesting evidence of transport inequality in service reliability. In the second study, I analyzed the spatio-temporal patterns of pass-ups during the COVID-19 pandemic in Winnipeg using emerging hot spot analysis. I found hot spots in the central and southern parts of the city, which coincide with low-income neighbourhoods. This finding suggests that socially disadvantaged neighbourhoods might experience inequality in terms of unreliable transit services, and this issue further worsened and persisted during the pandemic. Transit providers and city leaders could benefit from utilizing these methods to evaluate and improve transit services, making them more equitable for the populations that need them the most.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".