Safety effects of maintenance treatments to improve pavement condition on two-lane rural roads — insights for pavement management
Bibliographic record
Abstract
The research used data from two-lane rural roads in Ontario, Canada to evaluate the change in safety following maintenance treatments to improve pavement condition as measured by International Roughness Index (IRI). The state-of-the-art empirical Bayes (EB) before-after methodology was applied to estimate the effects on crashes, separately for arterial and collector roads. The results indicate statistically significant reductions (P < 0.05) in all crashes and property damage only (PDO) crashes of approximately 5% and 7%, respectively, for arterial roads and approximately 11% and 13% for collector roads. For fatal plus injury (FI) crashes, there were small, statistically insignificant changes for the two road types. The results provide interesting, and sometimes counterintuitive insights for those planning maintenance treatments to improve IRI. In sum, the results suggest that consideration should be given to designing and planning pavement maintenance treatments on a site-by-site basis, and, in so doing, to optimize the IRI levels and safety effects that may be accomplished with specific treatments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".