Researching a Capacity Model for Multilane Roundabouts with Negotiation of the Right-of-Way between Antagonist Traffic Flows
Bibliographic record
Abstract
This article summarizes a research program designed to assess operating conditions through capacity estimation at not-conventional roundabouts characterized by a central island with a large diameter and by two (or more) entering and circulating lanes. Although giving priority to vehicles on circulatory roadway is the nominal operating rule, on-field observations highlighted that in some infrastructural schemes of the type considered in this research drivers negotiate the right-of-way according to a consensus pattern that alternates between antagonist traffic flows, similarly to that observed at all-way-stop-controlled intersections. Considering the peculiarity of the way of working at roundabout schemes being examined, as well as difficulties faced in the application of models based on gap-acceptance theory to evaluate performances, an analytical capacity model derived from field observations was proposed for multilane not conventional roundabouts. Drawing inspiration from the iterative procedure proposed for capacity estimates at all-way-stop-controlled intersections, a calculation algorithm organized in 5 subsequent computational steps was developed in order to evaluate capacity at each entering/circulating lane for the type of roundabouts examined in this research. Results compared to those calculated by models for modern roundabouts show the effect on estimates of capacity raised by a more realistic operating pattern and indicate that the capacity model may be able to estimate parameters useful for planning and design purposes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".