Road Safety Audits and major P-3 freeway projects: estimating the reduction in collision frequencies
Bibliographic record
Abstract
Road Safety Audits (RSAs) include an independent review process that has increasingly been incorporated into the Design-Build-Operate model for (typically) large-scale freeway construction projects throughout Canada. While recent research has suggested that RSAs are a cost-effective means to improve safety on freeway (and other road) facilities, empirical evidence is lacking. This study undertook a comparison of observed and predicted collision frequencies from three large-scale Public-Private-Partnership (P-3) rural freeway projects with similar fundamental characteristics (e.g., functional classification, cross-sectional geometry, RSA team, and project budget). Collision reductions associated with the RSA process were estimated to develop a better understanding of the net safety impacts that may be attributed to the review process. The study results indicate that including RSAs in the development process of the freeway projects has reduced the overall frequency of collisions by 15% (or a collision modification factor of 0.85), which is equivalent to a reduction of 0.12 collisions per kilometre per year.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".