A Transportation Analytic Solution for Predicting Flight Cancellations
Bibliographic record
Abstract
Flight cancellations can negatively impact passengers and airlines by causing stress, time loss, financial losses, and providing a disruptive travelling experience. Airlines pay for crewmembers, provide refunds for passengers, and need to account for other unexpected expenses. Passengers might have a connection and need to get to a specific place for an important event such as a work conference, wedding, funeral, or vacation. Applying advanced transportation data analytical techniques to develop practical solutions can contribute to the ongoing development of more efficient and reliable air travel. In this paper, we present a data science solution, which integrates flight data, weather data, and other related data to determine key factors contributing to flight cancellations. In particular, we focus on weather-related factors such as precipitation and wind speed. Evaluation results on real data show the practicality and accuracy of our solution in predicting flight cancellations.
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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) | 0.000 | 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".