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Record W7055115907

Caractérisation de la complémentarité entre le vélo et le transport en commun

2024· other· fr· W7055115907 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2024
Typeother
Languagefr
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Filter (signal processing)Field (mathematics)Context (archaeology)Limiting
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: «Dans une société où l’automobile occupe une place prédominante, l’adoption de comportements de mobilité durable est urgente. La réduction de l’utilisation de l’automobile entraîne nécessairement une augmentation de l’usage des modes de transport durables comme les transports en commun, le vélo et d’autres alternatives. Toutefois, pour que cette transition soit efficace, une meilleure compréhension des interactions entre ces modes de transport est nécessaire. Parmi ces interactions, la complémentarité entre les transports en commun et le vélo apparaît comme une solution prometteuse pour réduire la dépendance à l’automobile. Actuellement, il manque des outils et méthodes efficaces pour mesurer l'accessibilité cyclable autour des stations de transport en commun, tout en prenant en compte le confort des cyclistes. L’objectif principal de cette étude est d’apporter une réflexion sur ces interactions et de proposer une méthodologie simple et reproductible pour évaluer l’accessibilité cyclable autour des stations de métro à Montréal, en intégrant les critères de confort cyclable. Cette méthodologie vise à identifier les zones où des interventions sur le réseau cyclable pourraient améliorer l’intégration entre vélo et transports en commun. L’étude repose sur l’évaluation de l’accessibilité cyclable actuelle des stations de métro de Montréal, en utilisant divers indicateurs. Des isochrones ont été générées pour mesurer l’accessibilité dans un rayon de 5, 10 et 15 minutes de vélo autour des stations. En parallèle, plusieurs scénarios d’amélioration du réseau cyclable ont été développés pour comprendre l’impact potentiel de ces améliorations.» ABSTRACT: «In a society where the automobile holds a predominant position, the need to adopt more sustainable mobility behaviors is urgent. Reducing car use necessarily leads to an increase in the use of sustainable modes of transport such as public transit, cycling, and other alternatives. However, for this transition to be effective, a better understanding of the interactions between these modes of transport is needed. Among these interactions, the complementarity between public transit and cycling appears to be a promising solution to reduce reliance on cars. Nevertheless, there is currently a lack of tools and methods to effectively measure cycling accessibility around public transit stations, while considering cyclist comfort. The main objective of this study is to reflect on these interactions and propose a simple and reproducible methodology to evaluate cycling accessibility around Montreal metro stations, integrating comfort criteria. This methodology aims to identify areas where cycling interventions can enhance integration with public transit. The study is based on evaluating the current cycling accessibility of Montreal metro stations using various indicators. Isochrones were generated to measure accessibility within 5, 10, and 15-minute cycling trips around metro stations. In parallel, several improvement scenarios were developed to understand the potential impact of network enhancements.»

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.332
Threshold uncertainty score0.659

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.007
GPT teacher head0.220
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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