Weather or Not to Walk: The Effect of Weather and Temporal Trends During Temperate and Winter on Sidewalk Pedestrian Volumes in Montreal, Canada
Notice bibliographique
Résumé
This study examines the impact of weather on pedestrian activity, as well as the temporal trends related to pedestrian trips in the city of Montreal, Canada. In particular, this study investigates the impact of extreme weather conditions during the winter season, and the difference in pedestrian trends between winter and other seasons. Six pedestrian counters were installed throughout the city of Montreal in zones which were either mainly commercial-service, or else mixed residential-commercial. The land-use mix surrounding the counter was taken into consideration in the investigation. The analysis was carried out separately over the months of April – November and December – March and as expected, the impact of different weather variables over different seasons was very significant. During the warmer months (April – November) humidity, and precipitation > 30 mm had the largest impact on pedestrian trips whereas during the winter, temperature, and precipitation affected the volume of pedestrian trips the most. The changes in volumes based on weekday / weekend were also quite different. In the winter months, pedestrian flows were much more sensitive to adverse weather during the weekend than the workweek. However, in the temperate months, the differences between weekday and weekend were less important. Pedestrian activity was also found to decrease with continued precipitation, or due to a lag effect of earlier precipitation. Overall, volumes of pedestrians decrease slightly in the winter compared with the more temperate months; however, morning and afternoon peak commuting periods remain the same regardless of season. Many different factors were controlled for in this study such as time of day, weekend / weekday, and the built environment surrounding each counter; however there are still factors which affect pedestrian trends which should be explored further.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».