What's a city got to do? Setting minimum transit-based jobs accessibility to enhance travel time equity and public transport mode share in Canadian cities
Bibliographic record
Abstract
In recent years, transport-planning practice has evolved from prioritizing car-centric development to focusing on advancing sustainability goals. Moreover, many cities around the world have begun to expressly establish time-based opportunity targets, seeking to adjust land use and transport systems to ensure people's needs are met. Accessibility, the ease of reaching destinations, is a performance-based measure that accounts for land use and transport systems and provides policymakers with a better understanding of how adequately cities provide their residents with access to opportunities. To guide communities in their aspirations to increase sustainable mode use as expressed in their local and regional transport plans, this chapter identifies the minimum levels of accessibility needed to improve travel-time equity (aiming for 60 minutes or less) and achieve the targeted public-transport mode shares in eight major Canadian metropolitan regions, including Toronto, Montréal, Vancouver, Edmonton, Québec City, Winnipeg, London and Halifax. The results show that the required level of transit-based accessibility is context-specific and depends on the size, built environment and spatial structure of respective regions, as well as the level of ambition that their goals represent. For many regions with relatively modest public-transport mode-share goals, the improvements in accessibility necessary to achieve their targets would require moderate efforts. However, to provide most residents with reasonable transit travel times of under an hour, the increase in transit accessibility would have to be more substantial.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".