Toronto's Future Rapid Transit Network: Evaluating Existing and Proposed Rapid Transit Services to a 2041 Forecast of Transit Needs and Transit Demands
Bibliographic record
Abstract
The Toronto Transit Commission (TTC), Toronto’s transit service provider, currently has 3 lines of rapid transit service intended for continued operation, and 4 new rapid transit projects that are underway. This study is to determine if the existing and new lines of rapid transit ideally meet present and future demands, and to locate spatial mismatch between demand and transit placement if they do not. Select socio-economic groups are also examined for their transit needs. These groups are selected by definitions of youth, elderly, language, recent immigrants, migrants, renters, and unemployed. Population projections to 2041 were examined to assess future outcomes. Catchment areas were employed around rapid transit stations when gauging transit line services. This was followed with statistical and mapping outcomes. These methods helped determine where demand for rapid transit is being adequately met now, and in the future, and where it might not be. Results show that downtown remains the densest area of rapid transit users. The Ontario Line has the greatest outcome in this study to provide service to a densely populated part of the city. In spite of this, existing lines seem to be better situated to meet future demand than the future lines. Transit lines in the network prove themselves to serve their purposes and target areas with high need and demand for transit services. Existing and new lines will experience higher growth rates than the rest of the city. Most of the proposed lines are shown to be developed in areas identified as coldspots. Other high demand clusters in the city remain unaddressed by the network. Albeit, the proposed lines do go a long way to providing much needed rapid transit to parts of the city. Overall, the densification of riders will increase more rapidly along present rapid transit lines, followed by the new lines. This study also identifies some spatial mismatch with respect to present and future lines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".