The Effects of a Free Bus Program on Travel Behaviour of Older Adults: A Case Study of a Canadian Suburban Municipality
Bibliographic record
Abstract
Public transit can be a potentially attractive alternative to driving for older adults, particularly for those who cannot drive due to health decline. Encouraging the shift of older adult travel behaviour from automobiles to transit could be accomplished through incentive programs. An example is the free bus/transit programs, which are more commonplace in Europe and are recently gaining popularity in Canada and the US. In this paper, short surveys (n = 131) and semi-structured interviews (n = 16) were utilized to explore older adults’ travel behaviour and experiences related to a recently introduced free transit program in the suburban municipality of Oakville, Ontario, Canada. Results from logistic regression models suggest that older adults with lower incomes, those who drive more during the week, and who live closer to downtown are more likely to have benefited from Oakville’s free bus program. The common reasons for using the program related to scheduling, the opportunity to produce and maintain social capital, financial savings and declining health. Some older adults did not use the program because they already had a subsidized monthly pass or when using bus was inconvenient. This research expands on a limited North American literature on the impacts of free bus programs among older adults. The North American population is aging rapidly, and most older adults would live in suburban communities in coming decades. In this context, the findings from this research may help planners and policy makers accommodate for older adults’ travel needs and preferences in suburban municipalities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".