Bibliographic record
Abstract
This study investigated the relationship between weather conditions and cycling ridership, as well as the hourly, daily, monthly, and yearly trends for use of urban bicycle facilities. A unique data set of cyclist ridership, collected at five automatic counting stations on primarily utilitarian bike facilities in the city of Montreal, Canada, was used. Absolute and relative ridership models were used to analyze the direct and lagging effects of weather variables and extreme weather conditions on hourly cyclist volumes. Precipitation, temperature, and humidity had significant effects on bicycle ridership. After other factors were controlled for, when the temperature doubled, a 43% to 50% increase in ridership could be expected; however, the temperature had a negative effect when it was higher than 28°C and humidity was greater than 60%. The results also showed that bicycle volumes in a given hour were significantly affected not only by the presence of rain in the same hour but also by the presence of rain in the previous 3 h or in the morning only. Daily bicycle volumes were 65% to 89% lower on weekend days than on Monday, the weekday with lowest ridership. This finding confirmed that the analyzed facilities were primarily utilitarian. Further, bicycle volumes peaked in the summer months, with an additional ridership of 32% to 39% with respect to April. Finally, bicycle volumes increased by approximately 20% to 27% in 2009 and 35% to 40% in 2010 with respect to 2008 in the cycling facilities under analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".