Opportunities and Barriers to Promoting Public Transit Use in a Midsize Canadian City
Bibliographic record
Abstract
This paper reports results from a survey of commute patterns of Queen’s University employees, the second largest employer based in the midsize city of Kingston, Ontario. Very few systematic analyses of travel behaviour have been reported for midsize cities (i.e., population 100,000 to 500,000). Our survey results indicate that the vast majority of the survey respondents remain firmly entrenched in using a private automobile as their primary commute mode. More than 50% of the employees commute by car, and only 5% commute by transit year round. An interesting finding is that there is some mode switching between private automobile and public transit by season, i.e. drive to work during spring and summer seasons and take public transit during fall and/or winter. These seasonal transit users could potentially be encouraged to use transit more regularly with appropriate interventions. The findings also reveal that unavailability of daily or weekly parking permits on campus forcesthe employees to purchase monthly car-parking permits. This is problematic since possession of a monthly parking permit becomes a strong motivation to drive to work regularly, and a strong barrier to even occasional use of public transit. The respondents suggested employer-subsidized transit passes, a more reliable transit schedule, and higher parking costs would encourage them to use public transit more.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".