The first national survey on transport poverty: Design, implementation and findings from Canada
Bibliographic record
Abstract
Transport poverty is a complex, multidimensional issue that remains loosely defined and inadequately measured. Persistent challenges related to data quality, consistency, and comparability, highlighted by the European Commission, continue to limit effective policymaking. Despite increasing attention to equity in transport research and practice, most countries still lack standardized national datasets that can capture the scale and severity of transport poverty.This paper presents the design and implementation of the first large-scale survey on transport poverty and transport-related social exclusion worldwide, developed by the Mobilizing Justice Partnership across all of Canada. We describe a multi-phase process involving collaborative workshops, input from a community advisory group, a pilot study, and a robust data collection strategy, including a comprehensive sampling and weighting process. The resulting dataset, covering over 27,000 respondents and openly available to researchers, decision-makers, and the public, offers extensive spatial and demographic coverage, enabling robust and policy-relevant analysis. A snapshot of findings reveals pronounced socioeconomic and demographic inequalities in Canada. Car ownership remains substantially lower among lower-income Canadians (under 70%) compared to higher-income groups (94%), while transit pass possession is more common among lower-income respondents. Approximately one in four participants experience modal dissonance, though its prevalence does not differ by income; instead, reasons behind this mismatch vary, with lower-income groups citing affordability constraints, whereas higher-income groups attribute mismatches to convenience and time considerations. Significant disparities in perceived safety were observed across gender, with non-binary individuals reporting the lowest levels of perceived safety, followed by women, and men reporting the highest. Satisfaction with transport conditions increases with age, with younger groups reporting consistently lower satisfaction. Racial disparities were evident in reported employment impacts, as Indigenous respondents and visible minorities were more likely to report declining job opportunities due to transport barriers. Housing affordability concerns were most acute among recent immigrants, who more frequently reported spending beyond their means on housing. Spatial analyses further demonstrate strong geographic variability in transport disadvantage, particularly in patterns of forced car ownership, suggesting the need for further exploration into the sociodemographic characteristics of this phenomenon, moving beyond solely built environment factors. Finally, accessibility to everyday destinations generally improves with city size, with larger metropolitan areas offering more consistent access than smaller towns and non-CMA areas.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".