Road Safety Audits: The North American Experience
Bibliographic record
Abstract
This paper describes how Road safety audits (RSAs) are a relatively new safety tool in North America. Since the first safety reviews in 1997, audits have become an accepted part of the design process for many large and/or safety-driven projects in Canada. In the United States, the FHWA is encouraging their use in federal, state, and municipal road projects. This paper discusses the application of RSAs in Canada and the United States, and the outlook for their future application. Recent milestones on the RSA road include the publication of the Canadian Road Safety Audit Guide (Transportation Association of Canada, 2001), and the first design-stage audit of a mega-project in the United States (the US$800 million Marquette Interchange in Milwaukee, in 2003). The paper discusses the evolution of audits in North America, and how the RSA process and report is changing in response to factors such as severe time constraints, owners’ and design teams’ attitudes, liability concerns, and safety concerns associated with sub-classes of road users such as cyclists and older drivers. This paper may be of interest to agencies who are inaugurating a RSA program in their jurisdictions, and to those sharing an interest in the progress and evolution of this versatile safety technique pioneered in the U.K. and Australia.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".