Evaluation of Airborne Precision Spacing in a Human-in-the-Loop Experiment
Bibliographic record
Abstract
A significant bottleneck in the current air traffic system occurs at the runway. Expanding airports and adding new runways will help solve this problem; however, this comes with significant costs: financially, politically and environmentally. A complementary solution is to safely increase the capacity of current runways. This can be achieved by precisely spacing aircraft at the runway threshold, with a resulting reduction in the spacing bu er required under today s operations. At NASA's Langley Research Center, the Airspace Systems program has been investigating airborne technologies and procedures that will assist the flight crew in achieving precise spacing behind another aircraft. A new spacing clearance allows the pilot to follow speed cues from a new on-board guidance system called Airborne Merging and Spacing for Terminal Arrivals (AMSTAR). AMSTAR receives Automatic Dependent Surveillance-Broadcast (ADS-B) reports from an assigned, leading aircraft and calculates the appropriate speed for the ownship to fly to achieve the desired spacing interval, time- or distance-based, at the runway threshold. Since the goal is overall system capacity, the speed guidance algorithm is designed to provide system-wide benefits and stability to a string of arriving aircraft. An experiment was recently performed at the NASA Langley Air Traffic Operations Laboratory (ATOL) to test the flexibility of Airborne Precision Spacing operations under a variety of operational conditions. These included several types of merge and approach geometries along with the complementary merging and in-trail operations. Twelve airline pilots and four controllers participated in this simulation. Performance and questionnaire data were collected from a total of eighty-four individual arrivals. The pilots were able to achieve precise spacing with a mean error of 0.5 seconds and a standard deviation of 4.7 seconds. No statistically significant di erences in spacing performance were found between in-trail and merging operations or among the three modeled airspaces. Questionnaire data showed general acceptance for both pilots and controllers. These results reinforce previous findings from full-mission simulation and flight evaluation of the in-trail operations. This paper reviews the results of this simulation in detail.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".