Guidance on Design and Application of Rumble Strips
Bibliographic record
Abstract
Many transportation agencies use shoulder rumble strips to address the problem of single-vehicle run-off-the-road crashes by alerting inattentive or drowsy motorists that their vehicles have drifted out of the travel lane. The application of rumble strips has expanded to include the installation of centerline rumble strips along the centerlines of undivided highways to reduce head-on and opposite-direction sideswipe crashes. Installing rumble strips along either the shoulder or centerline without considering the effect on other highway users (i.e., bicyclists and motorcyclists) may lead to unintended consequences. This research addresses a number of safety issues: (a) the safety effectiveness of shoulder rumble strips on different roadway types, (b) the safety effectiveness of shoulder rumble strip placement relative to the edgeline, (c) the safety effectiveness of centerline rumble strips on different roadway types, and (d) the safety effectiveness of centerline rumble strips along horizontal curves and tangents. The safety evaluations considered all severity levels (total crashes) and fatal and injury crashes. Statistical models for predicting noise levels in the passenger compartment of a vehicle for use in designing rumble strip patterns were also developed. The results of this research were combined with results from previous research to address important policy issues for transportation agencies to consider in the design and application of shoulder and centerline rumble strips.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".