Winter Cycling in North American Cities: Climate and Roadway Surface Conditions
Bibliographic record
Abstract
Winter and adverse weather are mentioned as part of the main deterrence of cycling. However, very little is known about winter cycling practice and its link to both weather and road surface conditions. Despite that cycling research has attracted a lot of attention in the last year, very little empirical evidences have been documented on winter cycling in North American cities. This paper investigates the cycling winter ridership patterns in a set of cycling facilities in three North American cities – Montreal, Vancouver and Portland. For this purpose, a winter cycling retention index is developed and compared across bike facilities in the three different cities, over various winter seasons. Moreover, a winter modeling approach is implemented to empirically quantify the effect of adverse weather conditions on cyclist activity during the winter months in these three cities. Finally, this paper explores the potential effect of winter surface conditions on bicycle ridership in Montreal, Canada. A simple cross sectional analysis in a set of facilities is carried on for this purpose. Among other things, it is found that winter cycling in North American cities like Montreal, Vancouver and Portland is an alternative mode of transportation for an important segment of the population. Among the important weather factors negatively affecting cycling are: high relative humidity with low temperatures, wind speed and precipitation (rain or snow) intensity. The lagged effect of precipitation is also observed in some cases. Sensitivity to weather conditions also varies across cities. Surface conditions also seem to be correlated to cycling retention – higher percentages of cyclists are retained when surface conditions are either “clean and dry” or “bare and wet” compared to snow covered, slushy or icy surface conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".