A surrogate safety assessment of scrambled phase intersections
Bibliographic record
Abstract
Traffic safety remains a top priority for policymakers and researchers, prompting numerous investigations to enhance safety for all road users, particularly pedestrians. This paper evaluates the safety impact of implementing scramble phases at intersections with a high concentration of vulnerable road users in Edmonton, Canada. The study spans three periods: prior to the scramble phase installation, immediately post-installation, and 6 months post-installation. The evaluation employs two key methodologies. Firstly, it observes the frequency of right-turn-on-red violations to assess driver behaviour. Secondly, the study investigates the frequency of serious conflicts, utilizing safety indicators such as time to collision, time difference to point of intersection, and distance between stop position and pedestrian. The findings suggest that introducing scramble phases positively impacts intersection safety, notably reducing severe conflicts and total right-turn-on-red violations. These results offer valuable insights for policymakers and researchers working towards safer urban traffic environments.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".