Investigation on Two-Stop-Line Signalized Roundabout: Capacity and Optimal Cycle Length
Bibliographic record
Abstract
Two-stop-line signalized roundabouts (TSLSR) are widely utilized in China. To calculate the capacity and optimal cycle length accurately, a model considering internal space constraint of the roundabout is developed in this paper. Firstly, the operational principle of TSLSR is analyzed. TSLSR is modeled as being equivalent to a four-phase intersection with left-turn protection phases, and the left-turn lane is further simplified as the “short-lane model”. Secondly, based on the above analysis, the capacity of TSLSR is modeled and additionally, the accuracy and sensitivity of the model are also analyzed. Finally, the optimal cycle length of TSLSR which maximizes the capacity is put forward. Results show that the accuracy of the capacity model is low without considering the internal space constraint of the roundabout. However, the model developed in this paper is shown to improve the accuracy (about 20%) and control the relative error to be within 10%. The parameter sensitivity analysis demonstrates that the cycle length and radius of the central island have a significant influence on the capacity of the left-turn lane and results in an optimal value of signal cycle length maximizing the capacity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".