Are Intersections With Cycle Tracks Safer? A Control-Case Study Based OnAutomated Surrogate Safety Analysis Using Video Data
Bibliographic record
Abstract
In recent years, cities in North America have been building cycle tracks with the intention of providing cyclists with a safer alternative to biking in the street. These facilities have been built and expanded but very little research has been done to investigate the safety impact of cycle tracks, in particular at intersections, where cyclists interact with turning-motor-vehicles. Some of the safety research has looked at observed injuries, finding some positive safety impacts of cycle tracks. The objective of this work is to investigate the safety effects of cycle tracks at intersections using a control-case study. For this purpose, a video-based method is proposed for analysing the post-encroachment time as a surrogate measure of the conflicts between cyclists and turning- vehicles traveling in the same direction. Using the city of Montreal as the case study, a sample of intersections with and without cycle tracks on the right and left sides were carefully selected accounting for intersection geometry and traffic volumes. A total of 90 hours of video were collected and processed in order to obtain cyclist and motor-vehicle trajectories and interactions. After cyclist and motor-vehicle interactions were defined, ordered logit models with random effects were developed to evaluate the safety effects of cycle tracks on conflicts at intersections. Among other results, it was found that intersection approaches with cycle tracks on the right are safer than intersection approaches with no cycle track; however, intersections with cycle tracks on the left compared to no cycle tracks were not found to have be significantly safer. As part of the contributions of this work, one can mention the extraction and use of disaggregate bicycle and vehicle flows in short time intervals such as 10 seconds intervals. The results identify that the likelihood of a cyclist being involved in a dangerous conflict increases with increasing turning- vehicle flow and decreases with cyclist group arrival.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".