Méthodologie d'évaluation du partage spatiotemporel de la rue
Bibliographic record
Abstract
RÉSUMÉ: De plus en plus de documents de planification, au Québec et ailleurs, contiennent des objectifs relatifs à l'amélioration du partage de la rue entre ses divers usagers : piétons, cyclistes, usagers du transport collectif, automobilistes, notamment. Plusieurs démarches visent également à rétablir un équilibre entre les différentes dimensions de la rue : la rue comme lien, c'est-à-dire comme espace dédié à la mobilité, mais aussi la rue comme lieu, c'est-à-dire comme espace d'activités, ainsi que la rue comme espace accueillant des fonctions environnementales. Ce partage de l'espace de la rue est le produit d'un arbitrage politique, administratif et technique entre les divers modes, arbitrage sous-tendu par des principes implicites ou explicites d'équité. De plus, le partage de l'espace urbain est changeant : il varie selon l'heure de la journée, le jour de la semaine et la période de l'année. Toutefois, peu de méthodes d'évaluation et d'indicateurs de suivi ont été proposés pour assurer la mise en oeuvre des objectifs publics en matière de partage de la rue. Alors même que les pouvoirs publics investissent des sommes colossales dans le réaménagement des infrastructures urbaines dans le but d'en améliorer le partage, il n'existe toujours pas de mesures à grande échelle pour étudier le caractère équitable de ce partage et pour en assurer le suivi dans le temps. Quelques méthodes ont été proposées, mais toutes ces méthodes dépendent de relevés effectués sur le terrain ou encore d'une classification manuelle des surfaces à l'aide de photos satellites. Il n'existe ainsi, à l'heure actuelle, aucune méthode systématique pour effectuer le diagnostic du partage de l'espace de la rue à l'échelle d'un territoire urbain. Le partage de l'espace urbain reste donc un concept difficile à définir, à mesurer et à évaluer dans le temps. ABSTRACT: More and more planning documents, in Québec and elsewhere, aim to reallocate street space between the various users of the street, namely pedestrians, cyclists, public transit riders and motorists. Several approaches also aim to restore a balance between the different dimensions of the street: the street as a link, i.e., a space dedicated to mobility, but also the street as a place, i.e., an activity space, as well as the street as a space dedicated to environmental functions. The allocation of street space is the product of a political, administrative and technical arbitration between the various modes and uses of the street, an arbitration underpinned by implicit or explicit principles of justice. In addition, the allocation of urban space is not fixed: it varies according to the time of day, the day of the week and the time of year. However, few evaluation methods and monitoring indicators have been proposed to oversee the realization of public goals in terms of street space reallocation. Even though authorities invest colossal sums in the redevelopment of urban streets, there are still no generalizable measures to study the equitable nature of these reallocation operations and to ensure their fairness or to monitor their evolution over time. A few methods have been proposed, but they depend on surveys carried out in the field or on a manual classification of surfaces using satellite photos. At the present time, there is no systematic method for carrying out the diagnosis of the sharing of street space at a city scale. The equity of street space allocation therefore remains a difficult concept to define, measure and evaluate over time.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".