Safety Evaluation of the SafetyEdge Treatment for Pavement Edge Drop-Offs on Two-Lane Rural Roads
Bibliographic record
Abstract
This study estimated crash modification factors (CMFs) for the SafetyEdge paving technique that is applied for the treatment of pavement edge drop-offs on two-lane rural highways. An empirical Bayes observational before-after evaluation based on installation data in Iowa, North Carolina, Ohio, Pennsylvania, and Florida found that the SafetyEdge treatment was associated with statistically significant reductions in fatal and injury (FI), run-off-the-road (ROR), opposite-direction (head-on and sideswipe), and drop-off-related crashes. The ROR CMF was also statistically significant for both horizontal curved and tangent sections. A disaggregate analysis examined the variation of the CMF for ROR with factors such as the travel lane width, traffic volume, presence of a horizontal curve, posted speed limit, and the pre-treatment ROR crash frequency. The results of that analysis indicated, for example, that the SafetyEdge paving technique appears to have a greater ROR safety benefit on two-lane rural roadway segments with average annual daily traffic volumes greater than 3,000 vehicles per day, relative to roadway segments with lower traffic volumes. A crash modification function (CMFunction) was calibrated with expected pre-treatment ROR crashes as the independent variable to simultaneously capture the relationship of the CMF for ROR crashes to multiple factors. An economic analysis found that the treatment is highly cost-effective.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".