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Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model
The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable."> MathJax.Hub.Config({ "HTML-CSS": { availableFonts: ["TeX"], preferredFonts: "TeX", webFont:"TeX", imageFont:"TeX", undefinedFamily:'Arial Unicode MS',serif", scale: 80, linebreaks: { automatic: true, width: "container" } }, "TeX": { extensions: ["noErrors.js"], noErrors: { inlineDelimiters: ["",""], multiLine: true, style: { "font-size": "90%", "text-align": "left", "color": "black", "padding": "1px 3px", "border": "1px solid" } } } }); MathJax.Hub.Register.StartupHook("End",function () { $(".art-abstract").css("display", "block"); }); (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','https://www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-5824718-2', 'auto'); ga('require', 'displayfeatures'); ga('send', 'pageview'); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-WPK7SW5'); _linkedin_partner_id = "2846186"; window._linkedin_data_partner_ids = window._linkedin_data_partner_ids || []; window._linkedin_data_partner_ids.push(_linkedin_partner_id); (function(){var s = document.getElementsByTagName("script")[0]; var b = document.createElement("script"); b.type = "text/javascript";b.async = true; b.src = "https://snap.licdn.com/li.lms-analytics/insight.min.js"; s.parentNode.insertBefore(b, s);})(); Previous Article in Journal
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Cite This PaperAbstractThe aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.View Full-TextKeywords: air traffic control; trajectory clustering; deep autoencoder; Gaussian mixture model air traffic control; trajectory clustering; deep autoencoder; Gaussian mixture model►▼Show Figures
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Get Informationclear img.review-status { width: 30px; } JSmol ViewerclearOpen AccessArticleAircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model byWeili Zeng 1,*, Zhengfeng Xu 1, Zhipeng Cai 1, Xiao Chu 1 and Xiaobo Lu 2 1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China2School of Automation, Southeast University, Nanjing 210096, China*Author to whom correspondence should be addressed. Academic Editors: Michael Schultz and Judith RosenowAerospace 2021, 8(9), 266; https://doi.org/10.3390/aerospace8090266 (registering DOI)Received: 23 July 2021 / Revised: 5 September 2021 / Accepted: 13 September 2021 / Published: 16 September 2021(This article belongs to the Collection Air Transportation—Operations and Management)View Full-TextDownload PDFBrowse FiguresCitation ExportBibTeX
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Cite This PaperAbstractThe aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.View Full-TextKeywords: air traffic control; trajectory clustering; deep autoencoder; Gaussian mixture model air traffic control; trajectory clustering; deep autoencoder; Gaussian mixture model►▼Show Figures
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedShare and CiteMDPI and ACS StyleZeng, W.; Xu, Z.; Cai, Z.; Chu, X.; Lu, X. Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model. Aerospace 2021, 8, 266.https://doi.org/10.3390/aerospace8090266
AMA StyleZeng W, Xu Z, Cai Z, Chu X, Lu X. Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model. Aerospace. 2021; 8(9):266.https://doi.org/10.3390/aerospace8090266
Chicago/Turabian StyleZeng, Weili, Zhengfeng Xu, Zhipeng Cai, Xiao Chu, and Xiaobo Lu. 2021. "Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model" Aerospace 8, no. 9: 266.https://doi.org/10.3390/aerospace8090266
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» Author: Weili Zeng
» Reference: Aerospace 2021, Vol. 8, Page 266
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737
