Safety Effectiveness of Centerline Rumble Strips in Kansas
Bibliographic record
Abstract
The occurrence of roadway departure crashes is a serious problem in the United States. These types of crashes correspond to approximately 40% of all crashes in the United States, and their estimated annual cost is $100 billion. The objective of this study was to quantify the safety effectiveness of center line rumble strips (CLRS) in Kansas. CLRS are raised or grooved patterns installed on the center of two-lane, undivided, rural highways to prevent mainly cross-over crashes, more specifically head-on and sideswipe in opposite direction types of crashes. In this study, 29 sections of highways with two patterns of CLRS (rectangular and football) were analyzed, totaling more than 590 km. The naïve and the empirical Bayes before-and-after methods were applied and compared. Results showed that following the installation of CLRS, total crashes judged to be correctable by CLRS were reduced by approximately 29%. Correctable crashes involving fatalities and injuries were reduced by approximately 34%. Cross-over crashes were reduced by approximately 67%. Run-off-the-road crashes were reduced by 19%. All comparisons except for run-off-the-road crashes were statistically significant. The two methods applied presented statistically similar results. There was no statistical differences between results from sections with rectangular or football shaped CLRS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".