New Concept Design of Directional Rumble Strips for Deterring Wrong-Way Freeway Entries
Bibliographic record
Abstract
Drivers who make wrong-way entries onto freeways pose a serious risk to the safety of other motorists and themselves. As a new countermeasure to mitigate the wrong-way entry issue, directional rumble strips (DRSs) were designed to generate elevated noises and vibrations to warn against wrong-way drivers and a normal level of stimuli to slow down right-way traffic. Five conceptual designs were developed based on Department of Transportation (DOT) guidelines, existing transverse rumble strips implementations, and input from rumble strip vendors. A national survey and extensive field tests were performed to verify the effectiveness of the proposed configurations. Acoustic and tactile signatures of the DRSs were measured by a specially equipped passenger car under different speed categories. The results indicated that the tested patterns could provide similar sound and vibration levels in the wrong-way direction as the existing transverse rumble strips (61.8–80.0 dBA sound signals and 1.1–1.4 g vibrations). The statistical and comparative analyses identified three DRS configurations that could produce greater audible and tactile signals in the wrong-way direction than the right-way direction, thereby serving the purpose of alerting inattentive wrong-way drivers while offering good visual attentiveness and applicability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".