The Impact of COVID-19 Pandemic on Air Transport Passenger Markets-Implications for Selected EU Airports Based on Time Series Models Analysis
Bibliographic record
Abstract
The COVID-19 pandemic caused a drastic reduction in air traffic passengers, especially during the period when the EU countries introduced a lockdown. Even after the relaunch of airline operators, passenger traffic did not return to the pre-pandemic trend. The aim of the study was to estimate the difference between the demand that was observed during the pandemic, and the demand that was forecast based on the pre-pandemic trend. The calculations were made for airports in selected EU countries. The first method was seasonality indicators, using quarterly data for 2015–2021. In the multiplicative model of seasonal fluctuations, the method of determining the seasonality indicators was used, based on the quotient of empirical values and the value of the trend. The one-name period trend method was used in the next step, then Fourier spectral analysis was applied. In the context of forecasts for the individual quarters of 2020 and 2021, all models indicate a further growing trend in the demand for passenger transport, which could have been observed if the COVID-19 pandemic had not occurred. As a result of the pandemic, the number of passengers handled at airports has significantly decreased. In the third quarter of 2021, freight growth was already noticeable, with the exception of Netherland, where a marked decline was recorded.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".