Elderly Mobility: Demographic and Spatial Analysis of Trip Making in the Hamilton CMA, Canada
Bibliographic record
Abstract
Recent interest in the urban transport challenges posed by the demographic outlook of ageing societies has prompted a growing body of scholarship on the subject. The focus of this paper is on the topic of elderly trip generation and the development of models to help formalise some important relationships between trip-making behaviour and personal, household and contextual variables (such as location). The case study is the Hamilton Metropolitan Area-an important functional component of Greater Toronto, itself one of the regions in Canada where the impact of ageing is expected to be most strongly felt. Using data from Toronto's Transport Tomorrow Survey and mixed ordered probit models, the study investigates the question of spatial and demographic variability in trip-making behaviour. The results support the proposition that trip-making propensity decreases with age. However, it is also found that this behaviour is not spatially homogeneous and in fact exhibits a large degree of variability-a finding that highlights both the challenges of planning transport for the elderly and the potential of spatial analytical approaches to improve transport modelling practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".