Online Stochastic Prediction of Mid-Flight Aircraft Trajectories
Bibliographic record
Abstract
Online trajectory prediction is central to the function of air traffic control of improving the flow of air traffic and preventing collisions, particularly considering the ever-increasing number of air travellers. In this paper, we propose an approach to predict the mid-flight trajectory of an aircraft using models learned from historical trajectories. The main idea is based on Hidden Markov Models, representing the location of aircraft as states and weather conditions as observations. Using our approach, one is able to make predictions of future positions of currently mid-flight aircraft for each minute into the future, optionally concatenating these positions to form the remaining predicted trajectory of an aircraft. We evaluated the effectiveness of the proposed approach using a dataset of historical trajectories for flights over the USA. Using prediction accuracy metrics from the aviation domain, we demonstrated that our approach could accurately predict trajectories of mid-flight aircraft, achieving an effectiveness improvement of 26% in horizontal error and 32% in vertical error over baseline models with virtually no loss in prediction efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".