FlightBERT: Binary Encoding Representation for Flight Trajectory Prediction
Bibliographic record
Abstract
Flight Trajectory Prediction (TP) is an essential task in Air Traffic Control (ATC). Currently, the TP task is usually achieved by regression approaches, which concatenates several scalar attributes of the observation into a low-dimensional vector as the inputs. However, it is difficult to accurately model aircraft motion patterns using low-dimensional features in complex and time-varying ATC environments. To improve the performance of the TP task, in this paper, a novel framework, called FlightBERT, is proposed based on Binary Encoding (BE) representation, which enables us to tackle the TP task as a multi binary classification problem. Specifically, the scalar attributes of the flight trajectory are encoded into binary codes and transformed into a high-dimensional representation by the attribute embedding module. Considering the prior knowledge among flight attributes, an Attribute Correlation Attention (ACoAtt) block is designed to explicitly capture the correlations among the specific attributes. A stacked Transformer block is applied to serve as the backbone network, which is followed by the predictor to generate the outputs. Considering the nature of flight trajectory, a hybrid constrained loss, i.e., combining the mean square error loss with the binary cross-entropy loss, is innovatively designed to optimize the proposed framework. The proposed method is validated on a large-scale dataset, which is collected from the real-world ATC environment. The experimental results demonstrate that the proposed method outperforms other baselines by quantitative and qualitative evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".