A Novel Methodology for Estimating the Capacity and Level of Service for the New Mega Elliptical Roundabout Intersection
Bibliographic record
Abstract
Mega elliptical roundabout is a new intersection on rural multilane highways. This intersection was developed in a previous paper using simulation data, and the authors found that it is better than interchange (full cloverleaf) in most scenarios of traffic flow. Basically, there are no guidelines or procedures for designing mega elliptical roundabout in AASHTO Green Book, Federal Highway Administration guides, and Highway Capacity Manual. Thus, the purpose of this study is to analyze the traffic operation performance and propose a methodology for calculating the capacity of mega elliptical roundabout and also the level of service by gap acceptance theory. Moreover, this research studied the influence of different values of truck ratios and also different values of a major highway speed on geometric design and traffic operation performance for mega elliptical roundabout. To validate the thoroughness of the proposed methodology, VISSIM simulations were conducted. This research will assist practitioners in determining the appropriate geometric design, assessing mega elliptical roundabout intersections, and making comparisons with other alternatives.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".