Evaluation and Optimization of Air Traffic Complexity Based on Resilience Metrics
Bibliographic record
Abstract
With the rapid growth of civil aviation, the increasing expansion of air traffic flow has brought serious challenges to the service capacity of the current airspace system, making the operation of the control sector increasingly complex. The accurate quantification of sector situational complexity is the basis for improving the service capability of airspace systems. The existing research on complexity ignores the resilience of the air traffic system in case of flight change, which cannot fully describe the dynamic characteristics of the air traffic situation. For this reason, a new air traffic complexity evaluation algorithm based on system resilience is proposed. Firstly, an air traffic situation network based on between-flight interaction is established. Then an overall sector complexity index based on network efficiency, average network failure rate, and average network recovery rate is built. Then, the complexity index is verified by analyzing the real radar number of ZSSSAR01 (sector 1 of Shanghai). By establishing a sector complexity optimization model, the complexity of sector air traffic and its volatility can be greatly reduced by changing the departure time of some flights. Finally, by optimizing the complexity of the sector, the workload of controllers is reduced, and the safety and efficiency of air traffic operations are improved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".