INVESTIGATION OF EUROPEAN AIR TRANSPORT TRAFFIC BY UTILITY‐BASED DECISION MODEL
Bibliographic record
Abstract
Air transport was traditionally a strictly regulated industry, dominated by national flag carriers and state‐owned airports. The global deregulation and liberalisation of air transport resulted in numerous changes, including the evolution of price competition, emergence of low‐cost airlines, growth in load factor, airport and airspace capacity problems, etc. Later, the internal market eliminated all commercial restrictions for airlines flying within the European Union (EU). Constraints on routes, number of flights, regulated tariff policies, etc. were removed. Since the issue of the third liberalisation package, EU airlines are permitted to provide air services on any route within the EU. As a result, prices have fallen dramatically, especially on the most popular routes. The air transport sector has had the highest rate of development recently. These issues are discussed in the introduction of this paper. The main scope is to investigate air passenger transport within Europe and to present the mathematical formulation of a disaggregate airport choice model created by the authors. A complex utility function‐based model has been developed and verified by the authors. The results of the model are in scope with experience in the real world. Santrauka Pastaraisiais metais pastebimas itin intensyvus transporto sektoriaus vystymasis, pasireiškiantis mažinamomis kainomis, nauju pigiu avialiniju atsiradimu bei ivairiu komerciniu apribojimu panaikinimu. Pagrindinis šio darbo tikslas yra ištirti keleiviu pervežima oro transportu Europos Sajungos ribose ir pristatyti pasirinkto atskiro oro uosto modelio matematine formuluote. Modelis, paremtas kompleksinemis panaudojimo funkcijomis, buvo patobulintas ir patikrintas pačiu autoriu, o gauti rezultatai atitinka realia patirti.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".