Synthesis of Practices for the Implementation of Centreline Rumble Strips
Bibliographic record
Abstract
This paper describes how agencies in North America are applying rumble strips along the centerlines of undivided two-way roads to reduce crossover collisions. This practice appears to be limited because of a lack of published knowledge regarding design practices, site selection for installation, expected benefits, and possible difficulties. In addition, there are no national guidelines in North America for the installation of centerline rumble strips (CLRS). In Canada, three provinces (Alberta, Saskatchewan, and British Columbia) have implemented centerline rumble strips. In the United States, according to a survey conducted in 2003, 20 states have installed CLRS, and 18 states are considering installations. The Transportation Association of Canada (TAC) Synthesis of Best Practices for the Implementation of Shoulder and Centerline Rumble Strips (2001) includes discussion of the initial benefits of CLRS and early knowledge of their implementation; additional information has become available since. iTRANS was commissioned by TAC to perform a technical review of recent CLRS research and current practices for CLRS implementation. The objective of the project is to prepare a synthesis of current practices, and recommend implementation guidelines. This project is in the process of national approval by the TAC Chief Engineers’ Council which represents the jurisdictional membership. This paper summarizes the characteristics of current centerline rumble strip applications in Canada and internationally. A discussion of the safety effectiveness and potential concerns, such as maintenance and driver behavior, as identified in current literature. Recommendations for the installation of CLRS are provided.
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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 | 0.156 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".