Board 2: Exploring Average Taxi Times at U.S. Hub Airports with ASDE-X
Bibliographic record
Abstract
Abstract Airport taxi times affect operation efficiency and congestion, fuel consumption, and aircraft emissions. Aircraft taxi time refer to the time it takes for an aircraft to move from the gate to takeoff, or to move from landing to the gate. Efforts to better understand and reduce airport taxi times may potentially improve airport capacity and reduce fuel usage, costs, and emissions. Airport Surface Detection Equipment, Model X (ASDE-X) is a surveillance system that provides location and movement information of aircraft and vehicles on the airport to air traffic controllers. ASDE-X was developed to reduce the Category A and B runway incursions at airports by providing continuous information of aircraft and vehicle location on airport movement areas. This equipment was implemented at 35 major U.S. airports. The FAA defines U.S. airports as Large, Medium, Small, and Non hub airports. In National Plan of Integrated Airport System (NPIAS), the Appendix A: List of NPIAS Airports provides a list of U.S. airports and their hub classifications (Small, Medium, and Large hub). The Aviation System Performance Metrics (ASPM) dataset from the FAA publishes airport and airline operation data of 77 airports in the U.S. In the dataset, the quarter-hour taxi-in time and quarter-hour taxi-out time of the 77 airports are given. In this study, the researchers explore the possible effect of ASDE-X implementation on airport taxi-times at 71 U.S. airports in the ASPM dataset. ASDE-X is installed at 35 airports and not installed in the other 36 airports in the ASPM dataset. In this paper, taxi times for the ASPM airports with ASDE-X are compared to the airports without ASDE-X. Identifying potential factors affecting airport taxi times may help researchers build better taxi time prediction model and may help airport managers to make better decisions to improve airport efficiency and capacity. Educators may use this research to teach large-sample data collection, data cleaning and consolidation, design of experiment, and statistical and graphical methods to answer research questions in undergraduate engineering courses. Undergraduate aerospace or aviation students may improve their comprehension of taxi times, ASDE-X, and airport operations from this research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".