Identifying TDM Corridors in Large Metropolitan Regions
Bibliographic record
Abstract
Transportation Demand Management (TDM) remains an active goal for many metropolitan regions. While much has been written about TDM methods and their impacts, less is in the literature about quantitative methods to identify corridors where TDM may be feasible and have the greatest impact. In this paper, the authors develop and implement quantitative techniques to identify locations where TDM initiatives are (and are not) feasible to shift demand in time, space or mode. The authors apply the approach to the Greater Toronto and Hamilton Area (GTHA) in Ontario Canada. The results demonstrate that the opportunities to shift auto demand in time are limited in the GTHA due to extended periods where demand reaches or exceeds capacity. By analyzing travel between mobility hubs (activity centers) in the GTHA, the authors identify several corridors where transit already provides service that is competitive with auto travel in terms of user costs. These corridors are strong candidates for improved performance through simple information provision. In other cases, transit or underutilized auto facilities may become competitive with existing congested freeways if effective road pricing initiatives are introduced. The results of the work can inform and prioritize TDM initiatives in the GTHA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".