Mesure de la cohabitation cycliste-piéton dans les rues piétonnes par analyse vidéo
Bibliographic record
Abstract
RÉSUMÉ: «RÉSUMÉ:Le transport actif se présente comme une solution face à plusieurs enjeux environnementaux émergents. En privilégiant des alternatives plus durables aux véhicules motorisés, le transport actif contribue à réduire leur utilisation, favorisant ainsi la création d’espaces plus durables. Dans ce contexte, notre étude se focalise sur un projet pilote de cohabitation entre cyclistes et piétons dans trois rues piétonnes de la ville de Montréal. Par le biais d’une analyse vidéo, notre objectif est d’étudier les interactions entre les cyclistes et les piétons au sein des zones piétonnières. Dans une première étape, nous avons examiné les vitesses et les accélérations de chaque cycliste circulant dans ces rues. Les résultats démontrent que le site Wellington et Hickson affiche les valeurs les plus élevées, avec une vitesse moyenne dépassant les 3.5 m/s (environ 12.6 km/h) et une accélération moyenne d’environ 1 m/s2, suivi de près par Bernard et Champagneur. Les autres sites présentent des similitudes ou une proximité des résultats. De plus, Wellington entre la 3è et la 4è avenue ainsi que Bernard et Outremont occupent les positions les plus favorables, avec les valeurs les plus basses. Ces deux sites génèrent des résultats similaires en termes de vitesses et d’accélérations, avec des moyennes d’environ 2.5 m/s (environ 9 km/h) et 0.4 m/s2, respectivement. Ensuite, nous nous concentrons sur les interactions entre cyclistes et piétons en utilisant des indicateurs de sécurité tels que le TTC (“Time To Collision”) et les distances minimales entre ces deux usagers pour chaque interaction. Les résultats obtenus mettent en évidence des variations significatives entre les sites d’une même rue, indépendamment des réglementations en place. Wellington et Hickson affiche systématiquement les valeurs les plus élevées pour les deux indicateurs, le qualifiant comme le site le plus sûr, tandis que Bernard et Champagneur ainsi que Mont-Royal et Drolet présentent régulièrement les valeurs les plus basses pour TTC15, avec des valeurs moyennes autour d’une seconde pour les deux sites.» ABSTRACT: «ABSTRACT: Active transportation presents itself as a solution to several emerging environmental challenges. By favoring more sustainable alternatives to motorized vehicles, active transportation contributes to reducing their use, thus promoting the creation of more ecological spaces. In this context, our study focuses on a pilot project of cohabitation between cyclists and pedestrians in three pedestrian streets in the City of Montreal, where cyclists are allowed to stay on their bikes in two of these streets. Through video analysis, our goal is to investigate the interactions between cyclists and pedestrians within pedestrian zones. Initially, we examined the speeds and accelerations of each cyclist moving in these streets. The results demonstrate that the Wellington and Hickson site exhibits the highest values, with an average speed exceeding 3.5 m/s (approximately 12.6 km/h) and an average acceleration of about 1 m/s2, closely followed by Bernard and Champagneur. Other sites show similarities or proximity in their results. Additionally, Wellington between the 3rd and 4th avenues and Bernard and Outremont occupy the most favorable positions, with the lowest values. Both sites generate similar results for speeds and accelerations, with averages around 2.5 m/s (approximately 9 km/h) and 0.4 m/s2, respectively. Next, we focus on interactions between cyclists and pedestrians using safety indicators such as “Time To Collision” (TTC) and the minimum distances between these two users for each interaction. The results highlight significant variations between sites on the same street, irrespective of existing regulations. Wellington and Hickson consistently display the highest values for both indicators, qualifying it as the safest site, while Bernard and Champagneur and Mont-Royal and Drolet regularly exhibit the lowest values for TTC, with average values around one second for both sites.»
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".