AHP-Based Approach for Location Planning of Pedestrian Zones: Application in Montréal, Canada
Bibliographic record
Abstract
Location planning for pedestrian zones is a multifaceted problem. The selection of best location from a list of potential stations involves consideration of different technical, economical, environmental, and social factors. For some of these factors, numerical values can be provided, but the others are based on the qualitative data. In this paper, a multicriteria decision analysis approach is presented based on analytic hierarchy process (AHP) for location planning of pedestrian zones under lack of quantitative data. A decision-making committee comprised of representatives of city transportation officials, public administration, and city residents or users is formed to select the criteria/subcriteria for evaluating pedestrian zones. Five locations for pedestrian zones are considered for evaluation in the city of Montréal: namely, Downtown, Jean Drapeau, Olympic stadium, Old Montréal, and Saint Denis. The results of this study indicate that the downtown area is the best choice for a pedestrian zone. Sensitivity analysis is conducted to determine the influence of criteria and subcriteria weights on the decision-making process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".