Optimizing geometric design of roundabouts: multi-objective analysis
Bibliographic record
Abstract
The main objectives of roundabout design are to maximize traffic safety and operational efficiency. Traditionally, because of the complexity of the system and the multiple objectives involved, the design process is iterative and time-consuming. A minor change in the geometry can result in significant changes in the system performance (operation and safety). This paper presents an optimization model that directly provides the roundabout geometry that optimizes two objectives: design consistency and operational efficiency. Design consistency is represented by the mean difference in operating speeds for various conflicting vehicle paths and operational efficiency is represented by the average roundabout delay. Vehicle paths (through, right, and left) and roundabout delay are modeled for all roundabout approaches. The input geometric data to the model can be easily obtained from an aerial photograph of the selected site using a geographic information systems (GIS) software. The system performance is optimized subject to geometric and traffic constraints. The proposed model is applicable to single-lane roundabouts (urban and rural) with four legs intersecting at right angles. Application of the model to an actual proposed roundabout site is presented. This proposed approach provides the optimum solution directly and is also more efficient than the traditional iterative approach. Key words: geometric design, roundabouts, horizontal curve, radius, optimization, consistency, capacity, traffic delay.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".