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 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 | 0.009 | 0.030 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".