Impact of Bicycle Boxes on Safety of Cyclists:A Case Study in Montreal
Bibliographic record
Abstract
This paper presents a methodology to evaluate the effectiveness of a bicycle treatment (bike boxes) at intersections using a before-after surrogate safety analysis based on longitudinal video-data analysis. As a surrogate safety measure, cyclists’ red-light violations are quantified for two periods before and two periods after the installation of a bicycle box at a signalized intersection in Montreal. For this purpose several hours of video were collected before and after the installation of the treatment. Based on the video data, red-light violations and potentially associated factors were collected for each cyclist that crossed the intersection, such as sex, age, group size, use of helmet, whether a cyclist stopped before crossing, vehicle-cyclist gap, etc. Violations with a short vehicle-cyclist gap were classified as dangerous (i.e., those situations in which cyclists pass the intersection during the red phase with a small vehicle gap). For the data analysis, a multinomial logit regression technique was used to identify the factors that increase or decrease the probability of cyclist violations as well as their changes over time. Both raw estimates and model estimates show that the presence of a bicycle box has a significant impact on the total number of cyclists’ violations; however, the impact on the number of dangerous violations is not clear. More video data from other intersections before and after the treatment implementation is required to validate these preliminary conclusions. Moreover, the video-data generation and surrogate approach proposed here can be applied to the evaluation of other bicycle treatments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".