Time Prediction of Key Links in Flight Ground Support Under Missing Data
Bibliographic record
Abstract
Accurate flight ground service time prediction can improve the flight transit efficiency and realize flight refinement management. However， the lack and abnormality of relevant data make the research more challenging in real scenarios. To this end， a flight ground service time prediction model considering missing values is proposed. A dynamic time warping（DTW） algorithm is introduced on the basis of the causal graph convolutional network （CGCN） to realize the prediction of flight ground service link time under different data missing modes and missing rates. The flight support dataset （6 480 items） of a large airport in China is used as an example for validation. The results show that the proposed model can maintain high prediction performance under conditions of 20%—80% missing rates， compared with the remaining seven benchmark models including causal graph convolutional network with missing data （CGCNM）， dynamic spatial⁃temporal graph convolution network （DSTGCN）， Bayesian temporal matrix factorization （BTMF）， long short⁃term memory （LSTM）， etc. The mean absolute error （MAE） of the prediction results for each service time node is reduced by more than 8.1%， and the root‑mean‑square error （RMSE） is reduced by more than 4.6%. The experiment demonstrates that the proposed model is better than the baseline model in terms of prediction accuracy and prediction stability. It can provide an objective and reliable decision-making basis for flight support operations.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".