How does Public Transit Serve Post-Secondary Students in Toronto? A Utility- based Analysis of Accessibility by Transit for Discretionary Trips
Bibliographic record
Abstract
Abstract Post-secondary students are a segment of the population whose activity-travel behaviour is not well understood. In particular, there is a relative dearth of studies that have examined the determinants of behaviours related to the participation of out-of-home activities among post-secondary students. This study uses data from a web-based survey administered to students attending universities in Toronto to examine the determinants of the location choices of these individuals when using transit to participate in discretionary activities. Additionally, count- and utility-based measures of accessibility by transit for university students in Toronto are calculated and compared. The specification of the location choice model offers insights into the determinants of location choice decisions made by university students for discretionary trips and highlight the impacts of transit level-of-service and land use attributes on location choice decisions. Moreover, the findings suggest that the impacts of these attributes can differ based on socio-demographic characteristics. The comparison of count- and utility-based accessibility measures underscore the shortcomings of the former, which stem from the treatment of all opportunities as equally attractive. The results of this study aim to contribute to the literature by offering insights into an aspect of the activity-travel behaviour of post-secondary students that has received relatively little attention. Similarly, the results of the study can be used to help inform planning decisions by shedding light on the activity-travel behaviour of a segment of the population that has typically been underrepresented in traditional household travel surveys.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".