Mobility Hub Guidelines: Tools for Achieving Successful Station Areas
Bibliographic record
Abstract
Metrolinx, the regional transportation agency for the Greater Toronto and Hamilton metropolitan area, has recently developed its first regional transportation plan. This plan identifies a system of connected mobility “hubs” to ensure the efficient coordination of land use and transportation planning. These mobility hubs generally comprise a rapid transit station and the walkable surrounding area. They serve as the origin, destination or transfer point for many trips and serve both a transport and a placemaking role. To direct the planning and development of these mobility hubs, Metrolinx recently developed the Mobility Hub Guidelines. This article summarizes the Guidelines. Two key aspects of the Guidelines are highlighted: (1) a typology for classifying the current and planned urban context and transportation function at a mobility hub; and (2) strategies to overcome challenges in achieving transportation and placemaking goals. Several case studies are provided.
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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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".