The Surrogate Safety Appraisal of the Unconventional Elliptical and Turbo Roundabouts
Bibliographic record
Abstract
Double-lane roundabouts have been created in many European countries over the past few centuries and are now characterized by an unsafe geometric development and by a low sustainability capacity or level. In this regard, new double-lane geometries have been implemented to overcome to these critical points. This article shows a comparison of two nonconventional double-lane roundabout schemes defined as elliptical and turbo. Considering this research on the unsafe and congested conditions for each road schemes at grade, the microsimulation approach allows comparing schemes of intersections not yet realized in order to be able to evaluate the critical issues. A symmetric traffic distribution and an identical vehicle mix for both design solutions are considered. The research was conducted considering two different double-lane roundabout-turbo roundabout and the elliptical roundabout. By comparing their geometry and technical elements, this article assumes that turbo roundabout due to its physical separating traffic lanes in the central circulatory carriageway will enable potentially better traffic safety conditions. This article has the following main goal: a comparison of traffic safety using VISSIM microsimulator and SSAM tools. The results can provide to show safety level on investigated scenario considering level of service (LOS) and also the possibility of obtaining time to collision (TTC) and postencroachment time (PET) through the use of surrogate parameters obtained by SSAM tool. In fact, the surrogate safety parameters allows evaluating the possible collision scenarios between them, according to the trajectories of the single vehicles. This assessment is useful in order to be able to evaluate by the local authorities which of the examined schemes can provide greater negativity in the construction and operation phase. Therefore this comparative analysis allows reducing, in the preliminary phase, possible security impacts and also economic ones for the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".