Embedding-Based Representation Learning for Forecasting Flight Characteristics
Bibliographic record
Abstract
Airlines face significant challenges when building flight schedules, particularly because of unpredictable operations caused by factors such as adverse weather, airport congestion, mechanical problems, and so forth. One of the major components of flight scheduling is block time; accurately estimating block time is crucial for optimizing resource utilization and effective planning (on the scale of minutes). Given that flight scheduling takes place months in advance, accurately predicting block time is a challenging task. This is largely a result of the limited availability of features affecting operations on a specific day, such as the weather, at the time of planning. Consequently, current literature suggests that popular machine learning models are not suitable and recommends the use of statistical historical metrics. However, these methods (a) do not capture the complex latent relationships between factors affecting block time, (b) do not effectively handle high-cardinality categorical data and temporal variations, and (c) only consider a very small number of flights in their conclusions. We conduct, to the best of our knowledge, the first large-scale study of the airline on-time performance database for 2018 from the Bureau of Transportation Statistics (BTS), a public dataset. Specifically, our work introduces an entity-embedding-based representation learning model to efficiently incorporate high-cardinality categorical features and improve the long-term predictive capabilities of the model. These entity embeddings also encapsulate richer feature representations and their interactions. Complementary to these, we conduct rigorous experimental evaluations across 10 baselines and significance tests to demonstrate the advantages of using our entity-embedding-based model to increase long-term forecast accuracy for planning. For reproducibility, the code has been made available at https://github.com/criticalml-uw/Embeddings-for-Block-Time-Prediction .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".