An Improved Runway Operation Capacity Model for V-Open Multirunway Airports in China
Bibliographic record
Abstract
Diverging runways are receiving considerable attention as replacements for parallel runways when constructing three or more runways in China. This has been prompted by the limitations associated with a single-direction airspace and associated regulations when using parallel runways. However, the operating efficiency of diverging runways is still greatly constrained by the separation standard for departing aircraft in CCAR-93TM-R5 and Decree of CAAC NO.123. To address these problems, an improved runway operation capacity model for a V-open multirunway airport is proposed to reduce the separation standard based on the equivalent lateral separation operation (ELSO) standard. A collision risk module is first proposed to assess the feasibility and safety of the proposed policy. Also, a novel runway capacity model for V-open multiple runways is then built to improve the operating efficiency for diverging runway. A series of comparative experiments based on actual flight data from Chengdu Tianfu International Airport demonstrated that reducing the divergence angle from 15° to 10° improved the runway operation capacity by about 8% based on the proposed improved policy. These research results could play a major role in facilitating appropriate amendments to the associated CAAC operation regulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".