0066 Bicyclist safety behaviours in an urban northeastern, united states city: an observational study
Bibliographic record
Abstract
Statement of purpose Bicycling is becoming a popular mode of transportation within the United States. Due to the increase of cyclists/vulnerable road users, laws and safety recommendations are being established to keep all road users safe. The purpose of this study was to observe bicycle safety behaviours and road law compliance of personal and bike share users at several high traffic intersections in Boston, MA. Methods/Approach This is a prospective observational study of safety and law compliance behaviours of bicyclists in downtown Boston. Investigators observed bicyclists for specific law and safety compliance behaviours including: wearing a helmet, riding in bike lanes, displaying reflectors at night, and obeying all road laws. The findings of this study were compared to other national and international studies that observed bike share and personal bike users. Results 1685 cyclists were observed. Of all observations, 74% of all bikers wore a helmet and 83% obeyed the road laws. 39% bike share riders and 78% of personal bikers were observed wearing a helmet (p = 0.0001). A sample of 680 cyclists was observed for obedience with mandated reflector laws at night and 39% were in compliance (p = 0.001). Conclusions Boston bicyclists were more compliant with helmet use and road law compliance as compared to prior studies in the US and Canada. The majority of cyclists demonstrated safe cycling practices, however other unsafe cycling behaviours were noted and merit further investigation (i.e. distracted biking). Bicycling is common mode of daily transportation, and more observational studies and interventional studies are needed to assess and mitigate emerging risks for bicyclists in today’s society. Significance and contribution to the field This is one of the first comprehensive bike observation studies in the city of Boston that provides data on multiple cycling safety behaviours. Based on our observations, the use of electronic devices while biking is widespread and requires further investigation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".