{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:19:41Z","timestamp":1755217181963,"version":"3.43.0"},"reference-count":37,"publisher":"American Institute of Aeronautics and Astronautics (AIAA)","issue":"8","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"am","delay-in-days":305,"URL":"https:\/\/www.aiaa.org\/userlicenses\/1.0\/#CompEndUserLicense"}],"funder":[{"DOI":"10.13039\/100006831","name":"U.S. Air Force","doi-asserted-by":"publisher","award":["FA8702-15-D-0001"],"award-info":[{"award-number":["FA8702-15-D-0001"]}],"id":[{"id":"10.13039\/100006831","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["arc.aiaa.org"],"crossmark-restriction":true},"short-container-title":["Journal of Aerospace Information Systems"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p> Realistic aircraft trajectory models are useful in the design and validation of air traffic management systems. Models of aircraft operated under instrument flight rules require capturing the variability inherent in how aircraft follow standard flight procedures. The variability in aircraft behavior differs among flight stages. In this paper, we propose a simple probabilistic model that can learn this variability from procedural data and flight tracks collected from radar surveillance data. For each segment, we use a Gaussian mixture model to learn the deviations of aircraft trajectories from their procedures. Given new procedures, we generate synthetic trajectories by sampling a series of deviations from the Gaussian mixture model and reconstructing the aircraft trajectory using the deviations and the procedures. We extend this method to capture pairwise correlations between aircraft and show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft. We demonstrate the proposed models on the arrival tracks and procedures of the John F. Kennedy International Airport. Distributional similarity between the original and the synthetic trajectory dataset was evaluated using the Jensen\u2013Shannon divergence between the empirical distributions of different variables, and we provide qualitative analyses of the synthetic trajectories generated. <\/jats:p>","DOI":"10.2514\/1.i011503","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T06:09:03Z","timestamp":1748844543000},"page":"730-740","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures"],"prefix":"10.2514","volume":"22","author":[{"given":"Soyeon","family":"Jung","sequence":"first","affiliation":[{"name":"Stanford University"}]},{"given":"Amelia","family":"Hardy","sequence":"additional","affiliation":[{"name":"Stanford University"}]},{"given":"Mykel J.","family":"Kochnederfer","sequence":"additional","affiliation":[{"name":"Stanford University"}]}],"member":"1387","reference":[{"key":"r1","unstructured":"\u201cConcept of Operations for the Next Generation Air Transportation System, Version 3.2,\u201d Joint Planning and Development Office, 2010."},{"key":"r2","unstructured":"\u201cSESAR 2020 Concept Of Operations Edition 2017,\u201d SESAR Joint Undertaking, 2020."},{"key":"r3","doi-asserted-by":"publisher","DOI":"10.2514\/6.1996-3766"},{"key":"r4","doi-asserted-by":"publisher","DOI":"10.2514\/6.1999-4233"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.2514\/2.4056"},{"key":"r7","doi-asserted-by":"publisher","DOI":"10.2514\/1.26750"},{"key":"r8","doi-asserted-by":"publisher","DOI":"10.2514\/1.53645"},{"key":"r9","unstructured":"KochenderferM. J.KucharJ. K.EspindleL. P.GriffithJ. \u201cUncorrelated Encounter Model of the National Airspace System, Version 1.0,\u201d Project Report ATC-345, Massachusetts Inst. of Technology, Lincoln Lab., 2008."},{"key":"r10","unstructured":"WeinertA. J.HarkleroadE. P.GriffithJ.EdwardsM. W.KochenderferM. 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