Benchmarking, Planning, and Promoting Transit- Oriented Intensification in Rapid Transit Station Areas: Project Key Indicators
Bibliographic record
Abstract
As population and employment in the Greater Golden Horseshoe (GGH) region increase, there is a need to continue investing in rapid transit infrastructure to connect people and jobs, reduce harmful greenhouse gas emissions from transportation, and ensure that congestion does not negatively affect Ontario’s economic growth. But for rapid transit to have a meaningful impact on shaping travel patterns in the region, new and existing rapid transit infrastructure projects must be integrated with land use planning to promote transit-oriented development (TOD). TOD can offer a number of quality of life benefits for individuals, and for planners and policymakers, TOD is a great way to maximize the return on investment from existing and new rapid transit infrastructure. However, there cannot be a one-size-fits-all approach to TOD in the GGH. With more than 400 rapid transit stations either in existence or in various stages of planning, there is considerable diversity in station area contexts throughout the region. The present project develops and applies an innovative planning tool that distils station area characteristics into a typology of similar station types. Next, this tool is applied to benchmark TOD in present and future rapid transit station areas in the GGH, identifying TOD performance and contrasting this performance with existing and proposed policy and planning to identify areas that can benefit from more targeted interventions. Finally, the project uses the information from the typology to perform a detailed case study of the Hamilton A-Line and B-Line LRT.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".