MétaCan
Menu
Back to cohort
Record W2133518753 · doi:10.2514/6.2011-6516

Modeling and Optimization of Terminal Airspace and Aircraft Arrival Subject To Weather Uncertainties

2011· article· en· W2133518753 on OpenAlexfundno aff
Maryam Kamgarpour, Wei Zhang, Claire J. Tomlin

Bibliographic record

VenueAIAA Guidance, Navigation, and Control Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsTerminal (telecommunication)Computer scienceAeronauticsAerospace engineeringAir traffic controlAtmospheric modelEnvironmental scienceMeteorologyArrival timeEngineeringTransport engineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

We develop an accurate model for the arrival traffic dynamics in terminal airspace that takes into account the weather forecast and runway configuration changes. The planning of runway configuration switching subject to weather constraints is formulated as a hybrid optimal control problem and a hierarchical approach is proposed to solve the problem. At the upper level of the hierarchy, a runway configuration sequence is determined based on weather forecast data and air traffic demand. At the lower level, the configuration switching times and the corresponding aircraft speeds are determined by solving a Mixed Integer Linear Program. We illustrate the utility of the approach by a case study inspired by operations in John F. Kennedy airport

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations5
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueAIAA Guidance, Navigation, and Control ConferenceSame topicAir Traffic Management and OptimizationFrench-language works237,207