Spatial Panel Model for Examining Airport Relationships within Multiairport Regions
Bibliographic record
Abstract
For better airport planning and air traffic management, local airport authority in a multi-airport region (MAR) often needs to consider the impacts of competition and collaboration with nearby airports on its own airport traffic. This paper proposed a dynamic spatial panel regression model to test the regional effects on airports in a MAR. The proposed model is applied to four closely situated airports in the Pearl River Delta region (PRD), China, to analyze their interactions, identify the determining factors, and evaluate the impact of these factors on airport capacity. PRD is one of the most prosperous areas in Asia, and competition among the four airports has intensified, due, in part, to the rapid growth of Guangzhou airport and Shenzhen airport. Together with the fact that Hong Kong airport reached 98% of its runway capacity in 2016, it is of great interest to understand the interactions among the airports in this region. The findings show that airport degree, flight frequency, airport capacity utilization, income, population, GDP, and fuel price are significant factors affecting airport’s capacity. Furthermore, there is a spatially lagged effect in income and population, and a time-lagged effect in airport capacity, GDP, and fuel price.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".