Commuter Cyclist Accident Patterns in Toronto and Ottawa
Bibliographic record
Abstract
In this study, self-reported cyclist collision and fall information from a mail-back questionnaire was analyzed for a sample of 2,945 adult cyclists who commute to work/school in Toronto and Ottawa. Analysis focused on incident frequencies by month, time of day, location, road surface condition, and injury level. These results are presented in order to provide a valuable complement to other sources of bicycle incident data obtained primarily from emergency room hospital records. Only a small percentage of collision and fall incidents resulted in a major injury and would therefore be found in a bicycle accident database compiled from emergency room hospital records. Slightly more, 19.2 and 11.7% of the collisions in Ottawa and Toronto, respectively, were reported to police. The results of the study found that collisions were more sensitive to automobile traffic, whereas falls were more sensitive to the prevailing roadway surface conditions. There was a higher proportion of falls than collisions during the winter months in both cities. However, the severity of injuries from collisions and falls were fairly consistent across time periods. Even when the severity of collisions and falls were considered for different roadway environmental conditions and between roads and off-road, no difference was found. This analysis suggests that minor collisions and falls should be considered in accessing the safety experience of bicyclists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".