Modeling Travel Tool Ownership of the Elderly Population: Latent Segmentation-Based Logit Model
Bibliographic record
Abstract
A latent segmentation-based logit (LSL) modeling framework investigated the travel tool ownership of the elderly population in the greater Toronto area, Ontario, Canada. This study utilized data from the 2006 Transportation Tomorrow Survey conducted in the greater Toronto area. The study developed five mutually exclusive travel tool ownership combinations or bundles for older adults. One of the unique features of this study is the development of an LSL model to capture unobserved heterogeneity in the older adults’ travel tool ownership decisions. The LSL model captures unobserved heterogeneity by allocating individuals to discrete latent segments through a latent segment allocation model. The model results suggest that the LSL model is estimated for two latent segments in which less frequent trip makers are more likely to belong to Segment 1 and more frequent trip makers have a higher probability of belonging to Segment 2. Moreover, the model results suggest that considerable heterogeneity exists among the older adults in the two segments. For instance, older adults residing in a neighborhood with a higher percentage of owned dwellings prefer to own multiple travel tools in one latent segment and reveal an opposite relationship in another segment. Moreover, older adults living closer to regional business centers have a higher probability of owning a monthly transit pass in one segment and exhibit an opposite relationship in another segment. This diversity in travel behavior should be addressed within the transportation and land use policies to ensure an effective and equitable transportation system for older adults.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".