Weather or Not to Walk: The Effect of Weather and Temporal Trends During Temperate and Winter on Sidewalk Pedestrian Volumes in Montreal, Canada
Bibliographic record
Abstract
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".