Combined Phase Design Model for Multileg Roundabout Intersections
Bibliographic record
Abstract
Multileg roundabout intersections are widely used in urban areas worldwide. However, its traffic organization and signal control are very complicated in the case of large traffic flow, and the signal optimization methods for conventional intersections are not suitable for applying directly to roundabouts due to the complexity of the operating process. Therefore, many scholars have focused on dedicated signal timing schemes for roundabouts, especially through tuning key parameters such as signal cycle and green light time. However, research and applications regarding variable phases at roundabouts are quite rare. To address this gap, this paper analyzes the traffic flow characteristics and capacity of multileg roundabouts and proposed a combined phase design model to achieve the maximum utilization of road capacity at roundabouts, which can generate the optimal phase scheme based on real-time traffic data. The feasibility of the combined phase design model is verified by a case study in Jinhua, China. The results indicate that the proposed combined phase design model can improve the applicability of actuated signal control and the reasonableness of signal timing for multileg roundabouts and thus further improve the roundabout efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".