Empirical Bayes Before-After Study to Develop Crash Modification Factors and Functions for High Friction Surface Treatments on Curves and Ramps
Bibliographic record
Abstract
The intent of the study was to fill a knowledge void by developing high quality crash modification factors (CMFs) and benefit/cost (B/C) ratios for high friction surface treatment (HFST). The state-of-the-art empirical Bayes (EB) before-after methodology was applied to evaluate the effects of this treatment on crashes of various types using data from West Virginia (curve sites), Pennsylvania (curve sites), Kentucky (curve and ramp sites), and Arkansas (ramp sites). The results for curve sites generally indicate substantial and highly significant safety benefits. This is especially so for the primary crash types targeted by HFST programs: run-off-road, wet road, and head-on side-swipe opposite direction crashes (HOSSOD). The results for ramp sites were inconsistent, with substantial benefits for all crashes and injury crashes for Kentucky, negligible effects for these crashes in Arkansas, and substantial and highly significant reductions in wet weather crashes in both states. A disaggregate analysis of the CMF results for curve sites indicated a logical and consistent relationship between CMFs and three variables: friction improvement, traffic volume, and expected crash frequency before treatment. These variables, and an innovative methodology, were used in developing crash modification functions (CMFunctions) that can be applied to determine where, and under what conditions, the treatment can be used most effectively. Such functions are typically not provided for the vast majority of treatments for which CMFs are available, so, in itself, developing them is a significant contribution of this research.
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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 | 0.076 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".