Deconflicted Air-Traffic Planning With Speed-Dependent Fuel-Consumption Formulation
Bibliographic record
Abstract
This paper discusses a unique formulation for the en-route flight planning problem in a constrained airspace with the objective to minimize costs incurred from earliness, lateness, and fuel-consumption, and to ensure flight safety. Mid-air conflict and collision avoidance, and also minimum separation distance between aircraft and speed-dependent fuel-consumption-rate, are explicitly formulated. A 3D mesh network consisting of waypoints is used to provide alternative routing options for aircraft. The formulation of fuel-consumption-rate as a function of speed as part of the air-traffic planning (ATP) problem is unique in the literature. Moreover, this paper is the first attempt to model the mid-air conflict and collision avoidance as part of the ATP problem. In order to demonstrate the capabilities of the mathematical model, test instances were generated and solved by three different solution strategies. The proposed centralized solution strategy can optimally solve small size instances, similar to the air-traffic around airports to help air-traffic control authorities to manage arrival and departure sequences. Larger networks that include several airports can be solved by the proposed two sequential solution strategies (decentralized and hybrid solution strategies) to help air-traffic planning authorities to manage air-traffic safely and more economically.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".