Exploring Changes Affecting Travel Behavior of Seniors
Bibliographic record
Abstract
In 2056, more than one quart of the Quebec population will be aged 65 years and older. Population aging is a worldwide issue and urban areas facing such intense phenomena will face multiple challenges, namely related to the provision of efficient and adapted social services such as transportation. Using data from five large-scale Origin-Destination travel surveys from the Montreal Area, covering 20 years, a pseudo-cohort analysis is conducted to document how features and behaviors of elderly are changing over time. Eights cohorts of people are studied using an age-period-cohort-characteristics modeling framework. Individual car access, non-motorization and transit share are modeled using this approach allowing to separate the effects due to aging, cohort (year of birth) and period (fundamental changes affecting all cohorts). Results show that age has a negative impact on car access but that there is an important positive period effect; non-motorization evolution is mainly due to aging while period and cohort effects are negative; age and cohort effects reduce transit share but the period one is now increasing since 1998 (generalized increase in transit share). The application of such models for prediction is also illustrated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".