Bibliographic record
Abstract
Airline revenue management is crucial for airlines to maintain their competitive position in the market. Revenue management addresses two main concerns in airline planning processes, pricing and seat inventory management, to balance supply and demand. Pricing or determination of airfare is a complex decision-making process influenced by factors including distance, number of passengers, market share, competition, and route-related characteristics. However, it is a central element as it impacts revenue generation, market positioning, demand management, cost recovery, and customer relationships. This study investigates the machine learning perspective on predicting airline market-level airfares and examines the determinants of airfare. In this regard, exploiting the publicly available data from the US Department of Transportation Bureau of Transportation Statistics, several supervised machine learning algorithms are tested and compared to obtain the most effective prediction for the given dataset. The Random Forest model outperformed the other models, with R adj 2 and RMSE scores of 0.998 and 1.811, respectively. An ad hoc feature importance analysis is also performed to gain further insight into the determinants of market-level airfares. The findings emphasize the importance of operational costs and pricing strategies in airfare prices.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".