Safety Effectiveness of Selected Treatments at Urban Signalized Intersections
Bibliographic record
Abstract
This study conducted a before–after evaluation by means of the empirical Bayes methodology for four types of treatments at signalized intersections with data from Winston-Salem, North Carolina. The results indicated that changing to protected left-turn phasing from permissive or permissive–protected phasing could lead to a virtual elimination of left-turn crashes but other crashes, which were likely to be less severe, could increase. Conversion from nighttime flashback to regular phasing seemed effective in reducing nighttime crashes. Replacing 8-in. signal heads with 12-in. heads seemed effective in reducing right-angle crashes, but this measure could increase other, less-severe crashes. Adding another red-signal lens to an existing one or changing from permissive to permissive–protected left-turn phasing did not seem particularly effective in reducing crashes, but these results were not definitive because they were based on a limited number of sites. Further research using data from other jurisdictions is needed, so that more definitive conclusions can be made about the safety effectiveness of these treatments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".