Accessibility, equity, and mode share: a comparative analysis across 11 Canadian metropolitan areas
Bibliographic record
Abstract
Transport FindingsAccessibility impacts mode choice and the degree of its impact varies between geographic regions and income groups.This paper presents an introductory analysis of this relationship for low and higher-income groups across 11 Canadian metropolitan areas.In all regions, low-income groups exhibit higher public transport use at the same level of accessibility.Additional differences exist between income groups in different regions when considering the change in mode share with varying accessibility.This study, while demonstrating the link between public transport mode share and accessibility, also begets further research to explain the differences in this relationship between groups in different regions. research question and hypothesesResearch has shown the importance of using factors associated with land use to explain public transport ridership (Cervero 1996;Dill et al. 2013;Foth, Manaugh, and El-Geneidy 2013;Schwanen and Mokhtarian 2005).In particular, some research has sought to examine the relationship between accessibility (or the ease of reaching destinations within a certain time threshold) and public transport mode share (Owen and Levinson 2018).Our research examines the relationship between public transport mode share and accessibility to jobs by public transport for low-and higher-income groups of individuals leaving their home census tracts in 11 Canadian metropolitan areas.We did this as an introductory analysis through a series of scatter plots and fitted curves to discern patterns.We hypothesize that public transport mode share is higher in denser regions and at higher levels of accessibility.We also expect that the low-income group will exhibit higher public transport use compared to the higher-income group at the same accessibility levels. methods and dataThe number of jobs in each census tract was obtained from the Statistics Canada Census flow tables.The tables present the number of commuters
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".