Ghostbusters: Hunting abnormal flights in Europe during COVID-19
Bibliographic record
Abstract
The impact of the COVID-19 pandemic is unprecedented in airline history, with irregular flight bans, the inability for accurate demand estimation, several turns in the epidemiological evolution, and a wide range of downstream effects on all aviation stakeholders. While most airlines have increasingly entered a recovery stage, compared to the utmost disruption around April 2020, the airline business is far from back-to-normality. Throughout the past two years, recurrent statements have been made regarding the existence of so-called ghost flights, where airlines operate nearly empty aircraft on markets with insufficient demand, partially with the aim to avoid losing precious airport slots. This study investigates the extent of such abnormal market service during the COVID-19 pandemic through an explorative, data-driven analysis, based on actual load factor data of European airlines for the years 2017 to 2021. We break down the observed deviations by airlines, markets, and airports. We find that low-cost carriers are most-likely to have performed abnormal flights during the pandemic; and that abnormal flights have mostly occurred on frequently-served markets. In addition, we show that airline responses, in terms of departure and yield changes, are largely heterogeneous across the 24 airlines in this study. Our study is the first one to shed light on the important issue of load factor deviations, and we hope that our findings can contribute to a better understanding of the existence of abnormal flights during the pandemic, as well as deriving appropriate policies for dealing with the ubiquitous threat and impact of ghost flights in the future.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".