Crowdsourced bicycling crashes and near misses: trends in Canadian cities
Bibliographic record
Abstract
Safety concerns are a barrier to increasing bicycling. BikeMaps.org, a tool for crowdsourcing bicycling collisions, near misses, and falls, offers rich data on local bicycling safety concerns. Our goal is to characterize dominant bicycling safety issues reported in nine Canadian cities. We analyzed 2,513 BikeMaps.org reports (522 collisions, 151 falls, 1840 near misses), and summarized the types of incidents reported, ratios of near misses to collisions by incident type and by city, and injuries resulting from various types of crashes. Incidents categorized as a ‘dangerous pass, overtake at midblock’, were most commonly reported and had the highest ratio of near misses to collision reports (9:1). Cities with a high commute mode share for bicycling had lower near miss to collision reporting ratios. Overall, 40.3% of reported collisions or falls required medical treatment. Incident types with the most severe outcomes were ‘left cross at an intersection’ (58.4% reported needing medical treatment); ‘vehicles failing to stop at intersection or yield to bike’ (54.0%); and ‘multi-use paths, vehicle conflicts at intersection’ (48.5%). Mitigating conditions leading to real or perceived concerns over dangerous passes by vehicles should improve bicycling comfort. Bicycling injuries will be reduced by safety improvements at intersections including those with multi-use paths.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".