Optimising total entry delay at roundabouts with unbalanced flow: a dynamic strategy for smart metering
Bibliographic record
Abstract
Modern roundabouts are widely used at intersections with light traffic, generally providing safety and other advantages. However, large entry delays are often observed at roundabouts with unbalanced flow patterns, even though the entry traffic flow is not high. A metering signal‐based strategy is examined to mitigate the above problems. A mathematical optimisation model is formulated firstly with the objective of minimising the total entry delay, subject to the metering signal thresholds. Then a solution algorithm based on VISSIM simulation is developed. Finally, a case study is carried out to testify the feasibility and applicability of the proposed model. Extended scenarios analyses under different levels of approach volume, different demand combinations and different proportions of right‐turn vehicles (left‐side driving) are also conducted. Results show that the methodology can effectively improve the operational performance, and a delay reduction of up to 25.7% can be expected using the metering signal‐based strategy. This can provide a criterion for the use of metering at the roundabout.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".