Integrating Safety and Human Factor Issues into Road Geometric Design Guidelines
Bibliographic record
Abstract
“Safe System” is a novel approach to road safety and represents a radical evolution in strategies for further improving road safety outcomes. Vision Zero and Advancing Sustainable Safety, recently developed in Sweden and the Netherlands, are two breakthrough paradigms of such an approach, including provisions aiming at road design, shifting a major share of the safety responsibility from road users to those who design the road transport systems. “Human Factors Guidelines for Road Systems”, an ongoing publication (8) which aims to provide the best factual information and insight on the characteristics of road users to facilitate safe roadway and operations decisions, is a benchmark current development for a safe user centered roadway design. In Road Safety Audits and Road Safety Inspections, considerations of safety and human factors have already been exploited. Specific guidelines and recommendations have also been developed to accommodate older road users, a group which is of growing importance. Roadway designers, who bear a major responsibility for the safe roadway design, should have a thorough understanding of the safety implications of their design decisions. Unfortunately, this kind of knowledge is limited among roadway designers due to the lack of appropriate professional training on safety and human factor issues, as well as the pertinent inadequacies in the University curricula. Consequently, there is an urgent need to aid roadway design engineers, in order to become familiar with current safety and human factors recent developments. The authors think that a promising way in achieving this objective is the effective integration of safety and human factors issues into road geometric design guidelines. In order to identify how safety and human factor considerations have been integrated in road geometric design guidelines, a critical review of current design policies in the United States of America, Germany, Canada and Australia was carried out and showed that these considerations have been incorporated to a varying degree into design guidelines. However, there are several areas of road geometric design that have to be enriched accordingly. On the basis of this review and the aforementioned recent developments, a framework is suggested and discussed that will contribute to integrating the existing knowledge regarding safety and human factors issues into road geometric design guidelines.
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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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".