TRAVEL DEMAND FORECASTING AND TDM MEASURES: THE EXAMPLE OF MONTREAL'S SOUTH SHORE, 2001-2021
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a case study of travel demand forecasting in the Montreal metropolitan area that is based on an extensive study conducted for the Quebec Department of Transportation. The main goal was to determine whether or not there is a need for new infrastructure vs. travel demand management (TDM) measures to solve the problem of growing congestion on Montreal's bridges. Findings from this work show that strong aging trends will have the effect of reducing past tendencies toward rising global mobility and motorization, and diminished demand should be evident by 2011, especially if strong TDM measures are instituted. The combination of various TDM measures could have a prominent effect on future travel demand and appears to be a realistic alternative to installation of new infrastructure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it