Systematic Analysis of Public Transit Data Availability in Canada
Bibliographic record
Abstract
Regional authorities will publish public transit route and timetable service offerings through the General Transit Feed Specification (GTFS) as a standard format. Systematically collected GTFS data can be used to study the structure, organization, and availability of public transit services nationally. In this work, we provide a systematic approach to collection of public transit data from various sources, preparation of a high-quality GTFS data inventory and analysis of the public transit offerings lending to numerous insights about transit availability at various geographic scales. Using Canada as a case study, we collected GTFS for the 213 candidate census subdivisions ([CSDs] representing cities, towns, municipalities, etc.) with populations greater than 20,000. These data were then cleaned and leveraged along with CSD-specific census statistics to comprehensively compare public transit offerings between provinces/territories and across CSD types. We determined that, despite a systematic collection process, the majority of CSDs lack official GTFS data, certain provinces are under- or over-represented in our analysis. We further proposed using the median of transit stop spatial density as a national baseline revealing that provinces such as Québec are severely lacking in public transit offerings. GTFS data analysis is insightful for understanding the current state and progress in urban public transportation, which is highly relevant to the United Nation’s Sustainability Development Goals. Our aggregated dataset and open-sourced codebase are publicly available at: github.com/chazingtheinfinite/canada-transit-study.
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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 | 0.021 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.054 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".