Bibliographic record
Abstract
It is largely consented that the bicycle is a sustainable mobility alternative in the city. Despite its many benefits, cycling comprises risks of injury or death. Among others, these risks are a result of unsafe overtaking manoeuvres performed by motorized vehicles against cyclists. This study aims to identify the characteristics of the road network and traffic influencing the lateral distance and duration of overtaking. Using bicycles equipped with distance sensors, GPS, and cameras, four cyclists covered 1689 km in Montréal. Hence, 3591 overtakings were identified with an average distance of 176 cm; 111 overtaking manoeuvres took place at distances less than 1 m, resulting in an unsafe event for every 32 overtakings. On average, the duration of an overtaking was 1.082 s and dangerous overtakings (less than one metre) lasted 0.57 s more than safe overtakings (one metre and over). A generalized additive logit model (GAM) is built to predict the likelihood of a dangerous lateral passing (less than 1 m). The results show that in taking a major route, the presence of parked vehicles and the time required for overtaking significantly increase the probability of experiencing a dangerous overtaking. However, the participant, type of vehicle, or presence of a bike lane have no significant effect. Therefore, the results demonstrate the importance of keeping cyclists isolated from traffic. Furthermore, providing a bike path along parking spaces seems to be a solution that does not enhance cyclist safety.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".