Reducing crossover accidents on Kansas highways using milled centerline rumble strips
Bibliographic record
Abstract
In the USA, shoulder rumble strips are very common. It is estimated that they reduce run-of-the-road crashes up to 25%. The Kansas Department of Transportation (KDOT) has installed rumble strips on the shoulders of almost all state highways in the state. However, Kansas has several miles of two-lane highways with no shoulder. These highways have a number of single vehicle run-of-the-road crashes (both sides) as well as crashes from cars going across the centerline and colliding with on-coming vehicles (crossover crashes). Some U.S. states have been using or experimenting with centerline rumble strips (CLRS). In most states that use them, they are used only on no-passing sections or curves. KDOT contracted with Kansas State University (KSU) to survey other states and summarize their experience and to develop a research design to evaluate KDOT test installations. KSU surveyed U.S. and Canadian provinces and found no serious negative problems with CLRS and recommended that they be field tested. KSU field tested several patterns of rumble strips, i.e., varying width and spacing. After selecting the best patterns, KDOT installed about 15 mi of two patterns of on the centerline of a two-lane state highway. Concurrently, the authors were contractors on an National Cooperative Highway Research Program (NCHRP) synthesis on U.S. and Canadian experience with CLRS. This K-TRAN report summarizes the findings of safety benefits and non-benefits from this nationwide survey. It describes research on the Kansas test patterns leading to recommendations on the best patterns, and the results and conclusions of field testing of these patterns regarding drivers’ acceptance and perceived benefits of CLRS. The overall conclusion of this study is that the safety benefits of CLRS outweigh some non-benefits and they are a viable, low-cost safety device for reducing cross over crashes on two-lane highways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".