Cycling rate trends from Canada’s national volunteer cycling count
Bibliographic record
Abstract
Pedal Poll/Sondo Vélo, Canada’s national volunteer bicycle count, completed its fourth count year in 2024. We analyzed this large, crowdsourced dataset of 204,584 people on bikes counted across 64 Canadian communities over four years to examine trends in cycling rates (people on bikes/hour) and their associations with cycling infrastructure type and accessibility to destinations. We classified the infrastructure at count sites according to the Canadian Bikeway Comfort and Safety (Can-BICS) classification system using Google Street View imagery and linked count sites to accessibility to destinations using national Spatial Access Measures (SAM) data. We used generalized linear mixed models to model the relationship between bicycle counts and the count year, count time of day, infrastructure at count sites, and accessibility to destinations, and included random effects for repeated sampling at the same count sites over time. We found that, relative to sites with no cycling infrastructure, medium and high comfort cycling infrastructure was associated with 55% and 105% higher cycling volumes respectively. Similarly, a 1 interquartile (IQR) increase in accessibility to destinations was associated with 65% percent increase in cycling volume. Relative to weekday morning counts, weekdays from 4–6 pm were associated with 53% higher cycling volumes, and weekends from 12–2 pm were associated with 28% higher cycling volumes. We did not see a change in the rate of people cycling over time at sites with medium or high comfort cycling infrastructure, but for the 112 count sessions at sites with low comfort infrastructure, each successive year was associated with a 12% decrease in cycling volume. These findings show that safe, high-comfort cycling infrastructure and accessibility to destinations are both associated with higher rates of cycling, and they highlight the value of a growing volunteer-collected dataset for advancing evidence on cycling in Canada. • Volunteer-collected cycling data provides insight into cycling in Canada. • High comfort cycling infrastructure is associated with higher rates of people cycling. • Accessibility to destinations by bike is associated with higher rates of people cycling.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".