Bicyclists’ Injuries and the Cycling Environment: The Impact of Route Infrastructure
Bibliographic record
Abstract
Safety concerns have contributed to low bicycling rates in North America. Injury rates are lower and cycling is more common in northern European countries where route infrastructure is designed for cyclists, yet few studies have examined the relationship between the cycling environment and injuries. A total of 690 people injured while cycling were recruited via emergency departments in Toronto and Vancouver, Canada. Conditional logistic regression compared route infrastructure at each injury site to that of a randomly selected control site from the same trip. The case-crossover design controlled for exposure to risk and for personal characteristics and other factors that are stable within a trip. Of 15 route types, cycle tracks (physically separated paths alongside city streets) had the lowest risk, about 9 times lower than the reference (arterials and collectors with parked cars and no bike infrastructure). Bike lanes on arterials and collectors with no parked cars, local streets, and off street bike paths had 2-fold risk reductions. Risks on arterials and collectors were lower when parked cars were not present. Other infrastructure characteristics were associated with increased risks: downhill grades; streetcar or train tracks; and construction. The results of this study indicate that the design approach used in northern Europe is effective in North America. The following route types are the best choices for common urban transportation locations and would lower injury risks to cyclists: alongside arterials and collectors – cycle tracks; on local streets – designated bikeways with traffic diversion; and off-street – bike paths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".