The Impacts of Universal Bus Pass on University Student Travel Behavior
Bibliographic record
Abstract
Many universities in North America have implemented, or are considering, universal bus passes (U-Pass) where all students pay a small fee and receive an unlimited transit pass. Previous studies have found increased bus use after a U-Pass is implemented, but it is not always clear who exactly is changing mode; car users ? active transportation users ? If active travel is reduced, one could argue that from a three-pillar sustainability perspective (economic, environmental, and health/society), it is not clear what is the overall impact of a universal bus pass. In this study, origin-destination surveys from before and after the implementation of a U-Pass for three universities in two cities in Canada are used to examine these questions: (1) for the student body as a whole, is the system more sustainable? (2) Do the observed changes in distances for walking follow the logic of travel time reduction? Our research has shown that the U-Pass programs implemented had the expected results of increasing the public transit share and decreasing the car share. However, the walk share to campus decreased importantly. Calculated indicators of sustainable development showed a mitigated effect of the U-Pass program with a reduction of cost for students to travel to university, a reduction of minutes of physical activity and no significant reduction of greenhouse gas emissions. These results suggest that beyond global sustainability, U-Pass programs should be put in place to answer specific transportation and planning objectives with careful attention drawn to local conditions that could influence its performance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".