Safety Evaluation of Centerline Rumble Strips: Crash and Driver Behavior Analysis
Bibliographic record
Abstract
The effectiveness of centerline rumble strips in reducing cross-over-the-centerline crashes and improving the safety of undivided roadways was evaluated. Twenty U.S. states, along with several Canadian provinces, are currently using centerline rumble strips. A detailed analysis of crashes on State Routes 2,20, and 88 in Massachusetts before and after installation of centerline rumble strips showed no significant change in crash frequencies; however, no fatal crashes have occurred on State Routes 2 and 88 since the installation of centerline rumble strips. Three cross-over-the-centerline fatal crashes did occur on State Route 20 after the centerline rumble strips were installed; centerline rumble strips were not a countermeasure to these specific crash types. Driver behavior at shoulder and centerline rumble strips was evaluated with a full-scale driving simulator. Drivers were found to react and correct the vehicle trajectory more quickly with centerline rumble strip encounters than with shoulder rumble strip encounters. About 27% of drivers made an initial leftward correction of their vehicles when encountering centerline rumble strips. Although this percentage may be inflated because of laboratory conditions, there is some probability of a driver confusing centerline rumble strips with shoulder rumble strips and reacting improperly. No improper (rightward) corrections were experienced with shoulder rumble strip scenarios. Centerline rumble strips were found to be effective at gaining drivers' attention and therefore to be an effective traffic control device and safety countermeasure in areas where a history of cross-over-the-centerline fatal and injury crashes occur.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".