Optimization Scenarios of Home-Work Distances in Montreal
Bibliographic record
Abstract
In Montreal, traffic congestion during peak hours is mainly caused by commuting trips. Many authors argue that a more effective management of car trips could help improve the situation. This paper presents optimisation scenarios of home-work distances in the Greater Montreal Area. The objective of this theoretical exercise is to assess the maximum reduction in home-work distances that could result from a “better choice” in home location by workers. By combining data from the 2008 Origin-Destination (OD) survey and the 2011 National Survey of Households, it is possible to assess the effectiveness of the delocalization of households.Three scenarios minimising total home-work distances are investigated: S1) reallocation of car-commuters while accounting for household size, S2) reallocation of car-commuters while accounting for household size and dwelling type and S3) reallocation of car-commuters while accounting for household size, dwelling type and tenure type (owned or rented dwelling). All scenarios are estimated at the municipal level. Moving workers to other home locations based on S1 reduces home-work distances by 58 %, down from 11,308,574 pers-km to 4,743,577 pers-km. As real travel distance between home and work are at least twice this distance, this represents a very important reduction. S2 reduces home-work distances to 5,424,141 pers-km: it is more restrictive but still accounts for a significant reduction potential. Finally, when estimating S3, the most restrictive scenario, total home-work distances are reduced by over 51 % (down to 5,718,749 pers-km). The paper examines the spatial structure of the results and provide ideas for policy implementation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".