A comparison of young and older adults’ attitudes and preferences towards different travel modes and residential characteristics: A study in Hamilton, Ontario
Bibliographic record
Abstract
Using Hamilton, Ontario as a case study, this study explores the difference between young (18–34 years) and older (65 + years) adults’ automobility behaviour (whether their most common mode of transportation was auto or not), by comparing their attitudes and preferences towards different travel modes. The study also investigates the differences in these two cohorts' attitudes and preferences towards residential characteristics since they can potentially impact travel behaviour. Exploratory analysis suggests that the difference between these two groups is marginal in terms of their attitudes towards driving. In general, young and older auto users both show similar attitudes towards different transportation modes. A similar trend has been seen for non‐auto users of young and older adults. The findings indicate young adults’ intention to shift towards an auto‐oriented culture, especially when they have a job. They also showed a preference for suburban living in the future. Older adults are mainly auto‐oriented; a small portion also seems pro‐transit. Transportation policies should consider these changing dynamics of travel behaviour among different generations. As attitudes and preferences influence travel behaviour to a greater extent, future studies should explore how attitudes and preferences can be modified to promote sustainable travel options.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".