Large-Scale Microscopic Traffic Behaviour and Safety Analysis of Québec Roundabout Design
Bibliographic record
Abstract
Roundabouts are a staple of European road design with many international studies demonstrating important reductions in collision severity and, to a lesser extent, frequency, among other benefits. With the promise of better safety, roundabouts have recently proliferated across across North America as well. However, regional adoption has not been smooth and questions still remain regarding roundabout design and suitability in the context of North American driving culture. Indeed, driving behaviour is a vital component of a well functioning roundabout as all movements within are managed entirely by driving etiquette. To obtain a better understanding of how roundabout design affects driving behaviour at Quebec roundabouts, a study of 37 instrumented weaving zones across 20 roundabouts throughout the province of Quebec was conducted. The instrumentation captured continuous, high-resolution, microscopic movements and speeds fifteen times per second (trajectories) of over 80,000 individual vehicles over a combined 9,500 veh-km, one of the largest studies of its kind to date. This study looks at the effects of several geometric design and built-environment factors on the behaviour and safety indicators of speed and time-to-collision. Among the major findings, roundabout conversions from traffic circles consistently scored the highest speeds and lowest (most dangerous) time-to-collisions, the number of roundabout lanes was negatively correlated with speed in the weaving zone, and mixed flow ratios between the roundabout lanes and the approach lanes produced the lowest time-to-collisions.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".