Evaluating the Impacts of Transportation Plans Using Accessibility Measures
Bibliographic record
Abstract
Despite a growing awareness of the social and environmental impacts of transportation infrastructure, most transport plans aim to increase mobility while paying little attention to accessibility.The shift to plannning for accessibility has been hampered by a lack of clear demonstrations of the usefulness of accessibility as a goal and norm for transportation planning.The purpose of this paper is to provide such a demonstration of the feasibility and value of accessibility evaluations with an analysis of Montréal's Transportation Plan.It shows how accessibility can be used by planners as a performance measure to evaluate a plan as a whole and to assess whether its goals will be attained, and/or to evaluate the plan on a project-by-project basis. L'ÉVALUATION DES PLANS DE TRANSPORT GRÂCE AUX MESURES D'ACCESSIBILITÉ RésuméMalgré une reconnaissance accrue des impacts sociaux et environnementaux des infrastructures de transport, la plupart des plans de transport visent à augmenter la mobilité et donnent peu d'attention à l'accessibilité.Dans la pratique, le passage d'une planification axée sur la mobilité à une planification axée sur l'accessibilité a été lent, en partie à cause d'un manque de démonstrations claires de l'utilité de l'accessibilité comme objectif et comme mesure dans la planification des transports.Le but de ce travail est d'offrir une telle démonstration de la faisabilité et valeur de l'évaluation de l'accessibilité grâce à une analyse du Plan de transport de Montréal.Il montre comment l'accessibilité peut être utilisée par les urbanistes et planificateurs comme mesure de performance, d'une part, pour évaluer un plan dans son ensemble et estimer la mesure dans laquelle il permettra d'atteindre des objectifs de durabilité sociale et environnementale et, d'autre part, pour évaluer un plan sur la base des projets individuels qu'il contient.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".