Safety valuation methods at urban atypical intersections. Analysis of infrastructural scenarios
Bibliographic record
Abstract
The definition of decision support tools for identifying ex ante evaluation issues for road safety measures is a new challenge for policymakers and designers managing road networks with the intention of improving traffic safety for roads of their competence.This is consistent with explicit strategies on sustainability in the transport sector established at European level, considering road safety as an essential prerequisite to warrant a sustainable mobility.Based on these considerations, the need for evaluation instruments of safety conditions both for existing infrastructural schemes and for new road installations is recently arisen so that the identification of risks involved in a particular road infrastructure is easier, as well as the definition of the priorities of intervention by means of technical measures.This is true in particular for road junctions characterized by a configuration not conforming to those ones recognized as safer.On this regard, the results of safety evaluations at roundabouts are not an exhaustive reference for geometric design where organizational schemes for circular intersections are similar to roundabouts but resulting from compromise choices as regards one or more geometric features of the roundabout.For this kind of atypical intersections specific safety problems are found, particularly in urban areas, due to existing constraints of different kind and the great variety of geometric layouts they take on.Moreover, in these cases the possibility of applying the safety methods proposed in the literature regarding traditional roundabouts, as well as the road safety audit procedures, is also compromised.Based on these considerations, in this paper ten case studies of this kind of intersections have been examined in order to verify the effectiveness of the risk analysis proposed by means of an infrastructural scenarios method.From the methodological point of view, the application of the accident delocalization procedure has allowed to highlight in relative terms the riskiness of different infrastructural scenarios arranging the organizational schemes of intersections.Moreover, the proposed method can represent a useful tool for safety diagnostic analysis being able to provide informations for policymakers and designers in order to address the infrastructural scenarios and the road schemes that need to be tested.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".