Emergence and Impact of Secondary Airports in the United States
Bibliographic record
Abstract
As major airports in the United States have reached their maximum capacity and became congested, available capacity at surrounding airports has been utilized by the emergence of secondary airports. Given the expectation of a larger number of operations in the National Airspace System (NAS) in the upcoming years, this trend of secondary airports emergence is likely to strengthen. In order to understand the dynamics of the regional airport systems, a study of the factors that led to the emergence of secondary airports was performed. The distribution of population at the regional level, the existence and the proximity of a secondary basin of population close to secondary airports were identified as major factors. Ground access and airport infrastructure were also enabling factors. The nature of the regional airport system, in terms of “Hub” versus “non-Hub” was also identified as a contributing factor. The entry of a low cost carrier was determined to be the essential stimulus in the emergence phenomenon. These entries modify the airport dynamics resulting in the stimulation of both local and peripheral markets. Following the entry of a low cost carrier several other carriers, both legacy and low cost, enter and consolidate the growth of the emerging airport. As a consequence of the emergence of secondary airports and their integration into a region wide multi-airport system, they induce impacts on the NAS structure. Recent consolidations of TRACONs (Terminal Area Control) were identified as primary impacts. As there will be increasing pressure of demand on core airports in the upcoming years, the development of additional secondary airports will be required. The transition from a single core airport to region wide multi-airport systems and the emergence of new secondary airports in existing multi-airport systems, impose new constraints that need to be taken into account in the NAS improvements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".