Transferability of safety inspection procedures for network-wide safety assessment of two-lane rural roads - an Italian-Hungarian experiment
Bibliographic record
Abstract
OBJECTIVES: The new EU Directive on Road Infrastructure Safety Management requires Member States to classify the road network into at least three categories according to its safety level. This study examines the application and transferability of the procedures between EU countries. METHODS: Our methodology consisted of two steps. First, we conducted a questionnaire survey among twenty Hungarian road safety inspectors, and second, we applied the Italian procedure to calculate the risk index and compare it with historical crash data. Two-lane rural roads were selected and divided into 200 m sections, excluding intersections. Road safety inspectors evaluated these using a matrix of 18 criteria based on video recordings. The risk index was calculated, together with a sensitivity analysis, and its consistency with the observed crash history was investigated. Finally, three homogeneous groups were identified using k-medoids cluster analysis. RESULTS: The survey showed good acceptance of the process, but we also found differences in how inspectors rated certain criteria. Our analysis of inspectors' ratings of severity showed that there were varying degrees of agreement. However, we also concluded that the three-level rating may help to reduce disagreement. Our risk index calculations used four years of crash data, and a moderate correlation between the crash rate and the risk index was found. By assigning a weighted average of adjacent sections and performing a k-medoids cluster analysis, we found that the optimal number of clusters is three, and these show a meaningful relationship with crash frequency. CONCLUSION: Regarding the application of the Italian procedure in Hungary to meet the requirements of the new EU RISM, the results are promising, and the lessons learned may also be useful for other countries.
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".