Sustainable Safety in the Netherlands
Bibliographic record
Abstract
This paper deals with prevention of human errors by proper road planning, road design, and improvement of existing roads within the framework of the Dutch Sustainable Safety vision. This vision focuses on three design principles for road networks and for roads and streets: functionality, homogeneity, and predictability. The ambition is to reduce considerably the number of crashes and casualties and maintain the Netherlands as one of the countries with the best road safety records. This vision was launched at the beginning of the 1990s and accepted as a formal part of Dutch policies in the mid-1990s. It resulted in a so-called Start-Up Program on Sustainable Safety, not only addressing the planning and design of road infrastructure but strongly emphasizing those aspects. Contents of the start-up program are described as the process leading to implementation. An overview presents different (road infrastructure) components of the start-up program and the estimated effects on road crashes. These components are functional road classification, 30-km/h zones and 60-km/h zones, safety of two-wheelers, and roundabouts. Evaluation studies suggest a 6% reduction in the number of fatalities and hospitalizations. Lessons learned will be used in defining the next phase. The start-up program has been used to draft new guidelines and recommendations for road planning and road design. An introduction of that is given. Finally, some thoughts are given about the next phase: how to proceed under circumstances in which fewer public funds will become available.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".