Analyzing Household Vehicle Ownership in the Japanese Local City: Case Study in Toyota City
Bibliographic record
Abstract
This study aims to understand the crucial factors affecting vehicle ownership in the local city, Japan. 14,855 household sample data in Toyota City are used as the research sample. The sample data are extracted from the 5th Person Trip Survey data in the Chukyo region. First, the unknown annual income is complemented by using an ordered probit model. Then, a trivariate ordered probit model is utilized to analyze ownership of light motor vehicles, ordinary motor vehicles, and small trucks simultaneously. To estimate unknown parameters effectively and efficiently, one type of Markov Chain Monte Carlo methods called the Gibbs Sampler algorithm is applied in this study. The significant findings suggest the following: (1) the annual income only affects the ownership of ordinary motor vehicles; (2) a household with a 60-year-old or older householder is more likely to own small trucks, compared to that with a householder below the age of 60; (3) the population density negatively affects the number of light motor vehicles and that of small trucks; (4) there is a substitution effect of vehicle ownership between light motor vehicles and small trucks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".