Evaluating, Comparing, and Improving Metro Networks: Application to Plans for Toronto, Canada
Bibliographic record
Abstract
As public transportation systems become more complex, an analysis of their network features can be of substantial help for planners. This work is an application of a network design model that was validated previously. It uses three indicators relevant to ridership: coverage, directness, and connectivity. Coverage calculates the percentage of land covered by the network. Directness relates to the convenience to travel, to avoid unnecessary transfers. Connectivity appreciates the structure of networks by measuring the affluence of transfer stations. According to the 15- and 25-year transit plans produced by the Toronto, Canada, regional transportation authority, Metrolinx, the objectives were to apply the model to evaluate these plans, compare them with other transit systems worldwide, and propose possible improvements. The model is applied only to the plans for the city of Toronto (seven light rail lines, three metro extensions, and one new metro line). These plans significantly improve the current system; for example, the model predicts approximately 546 million boardings per year for the 25-year plan, compared with 265.3 million currently. Nonetheless, seven possible improvements are also suggested, which might bring Toronto to approximately 619 million boardings per year, a 134% increase over current levels.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".