Factors influencing car ownership in Toronto: Insights from a hurdle-ordered model
Bibliographic record
Abstract
This study examines the effects of household socio-demographics, dwelling characteristics and the built environment on household auto ownership using data from a survey of 2007 households in the Greater Toronto Area (GTA). A two-stage hurdle model was constructed, consisting of a binary probit model of vehicle ownership (0 or 1+ cars) and an ordered probit model of fleet size (1, 2, or 3+ cars). Variables representing the five dimensions of density, diversity, destination accessibility, distance to transit, and design were included in the model. It was found that household socio-demographics and dwelling characteristics are the principal drivers of auto ownership, as shown by a greater number of variables significant at the 1% level and larger partial effects for these variables. Among the built environment variables, density, and destination accessibility were the most significant. Overall, the factors influencing ownership and fleet size were found to be the same, though there were some notable exceptions to this. These results show that policymakers seeking to reduce car dependency should focus on policies directly targeting the households, rather than those which target them indirectly through changes to the built environment around their residential location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".