Modeling Mobility Tool Ownership of Youth in Toronto, Ontario, Canada
Bibliographic record
Abstract
This paper presents the findings of the modeling of mobility tool ownership of the youth population in the greater Toronto and Hamilton area (GTHA) in Ontario, Canada. Research on mobility tool ownership is limited. The key contribution of the research is to enhance the understanding of how neighborhood characteristics and accessibility affect mobility tool ownership among GTHA youth. The study exclusively considered youth between the ages of 17 and 19 years; at this age the subjects passed through a transitional stage in the GTHA because of opportunities for ownership of types of mobility tools. This study used data from the 2006 Transportation Tomorrow Survey, a household-based travel survey of the GTHA. The study used a latent class choice modeling approach to account for unobserved heterogeneity, which was often ignored in traditional choice modeling. The results suggest that the latent class logit model outperforms the conventional multinomial logit model according to model fit and its ability to evaluate various parameters across latent classes. Several sociodemographic characteristics, trip attributes, accessibility measures, and neighborhood characteristics were found to explain different types of mobility tool ownership of the youth population. Finally, the results revealed that latent heterogeneity existed in the sampled population. The research offers important behavioral insights into the formation of travel habits of youth that could be useful in shaping the travel behavior of Toronto's young 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".