UTSC Commuting Patterns & Transit Reliability
Bibliographic record
Abstract
In this study we analyze how public transit facilitates access to the University of Toronto Scarborough (UTSC) campus. Our aim is to provide an overview of which services are most relevant to UTSC commuters; give some idea what transit trips to campus would look like for students, faculty, and staff; and explore potential barriers and challenges to transit use. In Section 2 we use the home locations of UTSC students, faculty, and staff to estimate the transit routes that commuters would most likely take to campus if everyone used transit. These routes are derived from a transport network dataset including current schedule data of transit agencies in the region (e.g. TTC, GO, DRT). We use this data to generate summary statistics and plots pertaining to trip durations, waiting times, number of transfers, and walking distances. We then use these estimated routes to create an interactive map which highlights critical transit routes and travel corridors. In Section 3 we take a close look at reliability on the most important TTC services for UTSC commuters: routes 38, 95, and 198. To do this, we make use of a large GPS dataset from the TTC which lets us observe arrival times at each stop over a period of six months. We compare express and local services in terms of speed and travel time variability, and then look at total travel time distributions between UTSC and selected points. Finally, we make some suggestions for strategies the TTC could use to improve service for UTSC commuters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".