Dangerous Overtaking of Cyclists in Montréal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it