Road Safety Audits in Croatia – Experiences to Date
Bibliographic record
Abstract
Road infrastructure is one of the key factors influencing road safety.Accordingly, the transport profession faces a growing need for safer road infrastructure each year.According to the National Road Safety Plan of the Republic of Croatia for the period 2021-2030, human, in combination with the road infrastructure, is a potential cause of approximately 35% of all serious road accidents.The solution lies in adopting "forgiving roads," which aim to accommodate driver errors while preventing or reducing the severity of traffic accidents.Road Safety Audits are one of the procedures aimed to achieve this goal.They were recognized and introduced through Directive 2008/96/EC of the European Parliament and the Council of 19 November 2008 on road infrastructure safety management.Authorized road safety auditors from the Faculty of Transport and Traffic Sciences, together with their audit teams, have conducted over 50 road safety audits in Croatia and abroad since the introduction of the road safety audit process.This paper presents the key results and conclusions derived from RSAs conducted.Analysis of the results of road infrastructure audits revealed that 5% of identified findings are high-risk, 30% are medium-risk, 64% are low-risk and 1% have no risk.The most common categories of identified issues fall into four groups: design elements, traffic signage, traffic light systems, and passive safety systems.Experience to date shows that over 75% of the auditors' findings are accepted by clients, with an 88% acceptance rate for proposals related to high-risk findings.The most common reasons for rejecting findings are increases in project costs and the need to amend location permits or construction documents that have already been obtained or are in progress.Based on the identified results, there is a clear potential for improving the current road design practices in Croatia.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| 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".