{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:02:55Z","timestamp":1784995375475,"version":"3.55.0"},"reference-count":35,"publisher":"American Institute of Aeronautics and Astronautics (AIAA)","issue":"2","funder":[{"name":"The Hong Kong Research Grants Council (RGC) General Research Fund","award":["15212622\/B-Q94L"],"award-info":[{"award-number":["15212622\/B-Q94L"]}]}],"content-domain":{"domain":["arc.aiaa.org"],"crossmark-restriction":true},"short-container-title":["Journal of Aerospace Information Systems"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p> Medium- and long-term four-dimensional (4D) aircraft trajectory prediction (TP) is a critical technology in air traffic management (ATM). This paper addresses the issue of existing medium- and long-term TP methods that are difficult to accurately fit aircraft trajectory data distributions. We propose a 4D TP method based on K-medoids clustering and conditional tabular generative adversarial networks (CTGAN), called C-CTGAN. Comparative experiments with four long short-term memory (LSTM)-based models and the original CTGAN model show that the proposed model\u2019s TP accuracy is significantly higher than others when predicting medium- and long-term trajectories. When using the trajectory datasets without holding and a prediction time span of 10 min, compared to the convolutional neural network (CNN)-LSTM model, the C-CTGAN model reduces the mean absolute errors (MAEs) of core trajectory parameters, such as latitude, longitude, geometric altitude, and ground speed, by 69.89, 15.00, 74.07, and 84.21%, respectively. Compared to the original CTGAN model, the MAE is reduced by 20.43, 39.09, 31.98, and 17.07%, respectively. When using the trajectory datasets with holding, compared to the CNN-LSTM model, the C-CTGAN model shows MAE reductions of 14.08, 23.68, 31.46, and 2.86%, respectively. Compared to the original CTGAN, the reduction is 34.88, 2.69, 23.16, and 73.91%, respectively. <\/jats:p>","DOI":"10.2514\/1.i011454","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T14:06:26Z","timestamp":1739196386000},"page":"90-102","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":7,"title":["Four-Dimensional Aircraft Trajectory Prediction with a Generative Deep Learning and Clustering Approach"],"prefix":"10.2514","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8351-7360","authenticated-orcid":false,"given":"Haoyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University (PolyU)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhizhao","family":"Liu","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University (PolyU)"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1387","reference":[{"key":"r2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2013.12.002"},{"key":"r4","doi-asserted-by":"publisher","DOI":"10.1080\/00207721.2014.966282"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103878"},{"key":"r6","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace9020091"},{"key":"r9","doi-asserted-by":"publisher","DOI":"10.1007\/978-4-431-54475-3_12"},{"key":"r10","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/2734763"},{"key":"r11","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/1208279"},{"key":"r12","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103919"},{"key":"r13","doi-asserted-by":"publisher","DOI":"10.3390\/su14073862"},{"key":"r14","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103704"},{"issue":"2","key":"r15","first-page":"418","volume":"46","author":"Qiao S.","year":"2018","journal-title":"Acta Electonica Sinica"},{"key":"r16","doi-asserted-by":"publisher","DOI":"10.1515\/jisys-2019-0077"},{"key":"r19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2015.06.001"},{"key":"r20","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2877572"},{"key":"r21","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.017"},{"key":"r23","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2023.104225"},{"key":"r24","first-page":"012003","author":"Ma Z.","year":"2019","journal-title":"Journal of Physics Conference Series"},{"key":"r25","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace8040115"},{"key":"r26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3016289"},{"key":"r28","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2021.3049404"},{"key":"r29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-19794-1"},{"key":"r30","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11213453"},{"key":"r37","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103554"},{"key":"r39","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2022.01304109"},{"key":"r40","unstructured":"XuL.SkoularidouM.Cuesta-InfanteA.VeeramachaneniK. \u201cModeling Tabular Data Using Conditional Gan,\u201d Advances in Neural Information Processing Systems. 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