A Novel Performance Framework and Methodology to Analyze the Impact of 4D Trajectory Based Operations in the Future Air Traffic Management System
Bibliographic record
Abstract
The introduction of new Air Traffic Management (ATM) concepts such as Trajectory Based Operations (TBO) may produce a significant impact in all performance areas, that is, safety, capacity, flight efficiency, and others. The performance framework in use today has been tailored to the operational needs of the current ATM system and must evolve to fulfill the new needs and challenges brought by the TBO content. This paper presents a novel performance assessment framework and methodology adapted to the TBO concept. This framework can assess the key performance areas (KPAs) of safety, capacity, and flight efficiency; equity and fairness are also considered in this research, in line with recent ATM trends. A case study is presented to show the applicability of the framework and to illustrate how some of the complex interdependencies among KPAs can be captured with the proposed approach. This case study explores the TBO concept of “strategic 4D trajectory deconfliction,” where the early separation tasks of 4D trajectories at multisector level are assessed. The framework presented in this paper could potentially support the target-setting and performance requirements identification that should be fulfilled in the future ATM system to ensure determined levels of performance.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".