Campus accessibility: the supply and demand of accessible transit for post secondary students in the Toronto Region
Bibliographic record
Abstract
Transit Accessibility is the temporally sensitive ratio between a transit system's supply over its ridership demand. Empirical measures of how well a system can meet the needs of its population is important for policymakers and planners to improve transit use within cities and reduce vehicle externalities. The requirement for accessibility is to reach a station in a reasonable time/distance, travel along the route and arriving at a final location with enough time to walk/reach your destination within a specified time. The 1-hour campus-commute trips of transit-dependent students are chosen as the subject in this study, exclusive to students and campuses in the Toronto Region. Accessibility is determined by a model which produces an Accessibility Ration Score (ARS). The ARS describes an area's accessibility of transit for specific population. The visualization of this data reveals potential shortcomings, opportunities, and successes, of Toronto's public transit system. The work suggests that significant improvements are needed in the existing transit system to be considered accessible to students. Keywords: planning, transportation, transit, urban planning, spatial analysis, GIS, GTFS, Network, systems, student, post-secondary, accessibility, access, commute, travel, campus, transit equity, education, Toronto
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".