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Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccu...
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Published in: | Applied sciences 2023-11, Vol.13 (21), p.11957 |
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description | With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China. |
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Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China.</description><identifier>ISSN: 2076-3417</identifier><identifier>EISSN: 2076-3417</identifier><identifier>DOI: 10.3390/app132111957</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>air traffic network ; Airports ; Civil aviation ; Cognition & reasoning ; complex network ; Eigenvalues ; Eigenvectors ; ensemble learning ; Methods ; Neural networks ; situation assessment ; Traffic flow</subject><ispartof>Applied sciences, 2023-11, Vol.13 (21), p.11957</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c363t-c2fb39d6ce6558191ffcb26bbf86d87d810b89c2932c6eaa3b086ce04bc0013f3</cites><orcidid>0000-0003-3782-0785</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2888111692/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2888111692?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,38516,43895,44590,74412,75126</link.rule.ids></links><search><creatorcontrib>Liu, Fei</creatorcontrib><creatorcontrib>Li, Jiawei</creatorcontrib><creatorcontrib>Wen, Xiangxi</creatorcontrib><creatorcontrib>Wang, Yu</creatorcontrib><creatorcontrib>Tong, Rongjia</creatorcontrib><creatorcontrib>Liu, Shubin</creatorcontrib><creatorcontrib>Chen, Daxiong</creatorcontrib><title>Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning</title><title>Applied sciences</title><description>With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China.</description><subject>air traffic network</subject><subject>Airports</subject><subject>Civil aviation</subject><subject>Cognition & reasoning</subject><subject>complex network</subject><subject>Eigenvalues</subject><subject>Eigenvectors</subject><subject>ensemble learning</subject><subject>Methods</subject><subject>Neural networks</subject><subject>situation assessment</subject><subject>Traffic flow</subject><issn>2076-3417</issn><issn>2076-3417</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1vEzEQXSGQqEpv_QGWuJLij11_HENUoFIEB9JDT9bYOw4OWXuxN4L--xqCUGcOM3p67-lppuuuGb0RwtD3MM9McMaYGdSL7oJTJVeiZ-rls_11d1XrgbYyTGhGL7qHb3E5wRJzIutasdYJ00JyIOtYyK5ACNGTD1BxJI2yydN8xN_kCy6_cvlBdt8xl0cCaSS3qeLkjki2CCXFtH_TvQpwrHj1b1529x9vd5vPq-3XT3eb9XblhRTLyvPghBmlRzkMmhkWgndcOhe0HLUaW0qnjedGcC8RQDiqG5n2zlPKRBCX3d3Zd8xwsHOJE5RHmyHav0Auewtlif6Ith-ldHyQXPOhl4CA1CjNes_koBio5vX27DWX_POEdbGHfCqpxbdca92OKw1vrJszaw_NNKaQlwK-9YhT9DlhiA1fK8UHwQYlm-DdWeBLrrVg-B-TUfvnefb588QTxDmKqw</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Liu, Fei</creator><creator>Li, Jiawei</creator><creator>Wen, Xiangxi</creator><creator>Wang, Yu</creator><creator>Tong, Rongjia</creator><creator>Liu, Shubin</creator><creator>Chen, Daxiong</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-3782-0785</orcidid></search><sort><creationdate>20231101</creationdate><title>Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning</title><author>Liu, Fei ; Li, Jiawei ; Wen, Xiangxi ; Wang, Yu ; Tong, Rongjia ; Liu, Shubin ; Chen, Daxiong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-c2fb39d6ce6558191ffcb26bbf86d87d810b89c2932c6eaa3b086ce04bc0013f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>air traffic network</topic><topic>Airports</topic><topic>Civil aviation</topic><topic>Cognition & reasoning</topic><topic>complex network</topic><topic>Eigenvalues</topic><topic>Eigenvectors</topic><topic>ensemble learning</topic><topic>Methods</topic><topic>Neural networks</topic><topic>situation assessment</topic><topic>Traffic flow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Fei</creatorcontrib><creatorcontrib>Li, Jiawei</creatorcontrib><creatorcontrib>Wen, Xiangxi</creatorcontrib><creatorcontrib>Wang, Yu</creatorcontrib><creatorcontrib>Tong, Rongjia</creatorcontrib><creatorcontrib>Liu, Shubin</creatorcontrib><creatorcontrib>Chen, Daxiong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Fei</au><au>Li, Jiawei</au><au>Wen, Xiangxi</au><au>Wang, Yu</au><au>Tong, Rongjia</au><au>Liu, Shubin</au><au>Chen, Daxiong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning</atitle><jtitle>Applied sciences</jtitle><date>2023-11-01</date><risdate>2023</risdate><volume>13</volume><issue>21</issue><spage>11957</spage><pages>11957-</pages><issn>2076-3417</issn><eissn>2076-3417</eissn><abstract>With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/app132111957</doi><orcidid>https://orcid.org/0000-0003-3782-0785</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | air traffic network Airports Civil aviation Cognition & reasoning complex network Eigenvalues Eigenvectors ensemble learning Methods Neural networks situation assessment Traffic flow |
title | Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning |
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