Evaluating the Potential for Centreline Rumble Strips on Arterial Two-Lane Highways of Rural New Brunswick
Bibliographic record
Abstract
This study represents a digital mapping approach to identify the potential for establishing centreline rumble strips on arterial two-lane highways in rural New Brunswick. Right-hand side rumble strips are employed on four-lane highways in New Brunswick, but the province currently does not employ any centreline rumble strips on its arterial two-lane network. This project employed TAC centreline rumble strip guidelines, in concert with GIS mapping, Google Maps, and collision data to estimate the number of kilometres (by route number) that would be eligible for centreline rumble strips and, by extension, the number of collisions that may have been prevented. There are a total of 1257 km of arterial two-lane highway in New Brunswick, with 466.6 km and 11.5 km falling outside of municipal boundaries (i.e. away from urban areas and communities) and on bridge decks respectively. Based on estimates of passing/no-passing zones on select arterial highways, approximately 273 km of centreline rumble strips would be needed. A total of 135 head-on and opposite-direction sideswipe collisions (where vehicles crossed the centreline) were recorded between 2007 and 2012 on the two-lane arterial highway network, and research suggests that up to 30% of such collisions may be prevented by centreline rumble strips. Due to the relatively low cost of rumble strip installation, in concert with prospective benefits of reduced collisions, centreline rumble strips have considerable potential in New Brunswick and should be considered for installation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".