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Record W2809769650 · doi:10.1155/2018/6376417

Waypoint Position Optimization for Accurate Ground-Based Arrival Time Control of Aircraft

2018· article· en· W2809769650 on OpenAlexvenueno aff
Noboru Takeichi

Bibliographic record

VenueJournal of Advanced Transportation · 2018
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsWaypointArrival timePosition (finance)Time of arrivalTrajectory optimizationComputer scienceMultilaterationTrajectorySimulationControl theory (sociology)Real-time computingEngineeringOptimal controlControl (management)Mathematical optimizationMathematics

Abstract

fetched live from OpenAlex

In the ground-based arrival time control operation, the measurement of the arrival time error and the speed advisory to cancel it are performed at specific waypoint(s). It is generally recognized that the arrival time accuracy depends on the accuracy of trajectory prediction and control and the availability of information about the aircraft intent. Additionally, the position of the waypoint also determines the arrival time accuracy. Therefore, in this study, the feasibility of waypoint position optimization to improve the arrival time accuracy of aircraft controlled by ground-based speed advisory and its effectiveness are discussed. The models of flight time uncertainty and the arrival time control window were developed using the actual track and numerical weather forecast data. An optimization problem was formulated that defined the arrival time uncertainty at the terminal point as the objective function and numerically solved using sequential quadratic programming. Through numerical investigations, the feasibility of the waypoint position optimization and its effectiveness were clearly demonstrated in comparison with uniform arrangement cases. It was also clarified that the advantage of the multiple waypoint application was enhanced when larger arrival time control windows were available.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.211
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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