Trip Generation of Vulnerable Populations in Three Canadian Cities: Spatial Ordered Probit Approach
Bibliographic record
Abstract
This paper provides an analysis of trip generation of three vulnerable groups: single-parent families, low income households, and the elderly. It compares mobility of these groups to that of the general population in three Canadian urban areas of Hamilton, Montreal and Toronto, based on data from large-sample metropolitan transport surveys. Ordered probit models with spatially expanded coefficients are used for the analysis. The ordered probit model addresses several short-comings of multivariate regression, and the spatial expansion of coefficients allows for comparisons of mobility rates among vulnerable populations over space. Trip generation rates of the elderly and single parents are found to have a stronger positive correlation with auto ownership and employment than the rest of the population. Trip generation rates of the low income individuals are more positively correlated with transit access. Spatial expansion shows that there are spatial mobility trends within each city for elderly populations even after socio-economic attributes are accounted for. In Hamilton and Montreal, mobility decreases in suburban areas. In Toronto, the lowest mobility for the elderly is found in the northwest. Such spatial differences are not found for single parent families. For low income households in Montreal, lowest levels of mobility occur north and south of the Montreal CBD. This spatial analysis provides clues as to where vulnerable populations may experience greater degrees of social exclusion. Further investigation in these areas could help prioritize transportation infrastructure projects or other social programs to account for the needs of vulnerable populations with the lowest levels of mobility.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".