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A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information

Shanmei Li, Dengjiang Sun, Chao Wang, Siying Xu, Yang Yang, Cheng Cheng Orcid Logo

IEEE Transactions on Vehicular Technology, Volume: 74, Issue: 3, Pages: 3793 - 3803

Swansea University Author: Cheng Cheng Orcid Logo

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DOI (Published version): 10.1109/tvt.2024.3487895

Abstract

Airport delay prediction plays a crucial role in air traffic management practices, including rerouting aircraft, implementing ground delays, and sequencing arrivals. This task is challenging due to the inherent nonlinear characteristics in traffic evolution. The popular deep learning-based traffic p...

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Published in: IEEE Transactions on Vehicular Technology
ISSN: 0018-9545 1939-9359
Published: IEEE 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68480
first_indexed 2024-12-06T19:47:03Z
last_indexed 2025-03-19T05:26:36Z
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In this work, we propose a novel three-channel Temporal Convolution Network (TCN) framework incorporating temporal complex network feature information for airport delay prediction. The complex network feature sequence of the temporal networks can effectively capture the nonlinear dynamic behavior of airport delays. Firstly, we divide the original time series of airport delays into three distinct series (current, daily, and weekly) and feed them into three channels of our model. This operation allows the model to effectively capture the inherent characteristics of local proximity and global periodicity in airport delay time series. Secondly, the complex networks are converted from airport delay time series using complex network theory, and the topological features of networks are combined with three-chamel TCN, to improve the ability of learning short-term nonlinearity of airport delay evolution. Finally, we incorporate weather condition and flight schedule information as external features to further enhance the prediction accuracy. We perform extensive experiments on a flight dataset at Hartsfield-Jackson Atlanta International Airport (ATL), and the results demonstrate the superiority of our approach compared to existing benchmark methods.</abstract><type>Journal Article</type><journal>IEEE Transactions on Vehicular Technology</journal><volume>74</volume><journalNumber>3</journalNumber><paginationStart>3793</paginationStart><paginationEnd>3803</paginationEnd><publisher>IEEE</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0018-9545</issnPrint><issnElectronic>1939-9359</issnElectronic><keywords>Delays, Airports, Atmospheric modeling, Predictive models, Time series analysis, Complex networks, Meteorology, Computational modeling, Air traffic control, Accuracy</keywords><publishedDay>1</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-03-01</publishedDate><doi>10.1109/tvt.2024.3487895</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm>Not Required</apcterm><funders>This work is supported by the National Natural Science Foundation of China under Grant No. 71801215, the Key Program of Tianjin Science and Technology Plan under Grant No.21JCZDJC00840 and 21JCZDJC00780, the Fundamental Research Funds for the Central Universities under Grant No. 312202YY02 and 3122018D026, and the Civil Aviation Administration of China Safety Capacity Building Fund under Grant No.SKZ49420220027.</funders><projectreference/><lastEdited>2025-03-18T15:48:39.3103735</lastEdited><Created>2024-12-06T13:10:29.6216149</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Shanmei</firstname><surname>Li</surname><order>1</order></author><author><firstname>Dengjiang</firstname><surname>Sun</surname><order>2</order></author><author><firstname>Chao</firstname><surname>Wang</surname><order>3</order></author><author><firstname>Siying</firstname><surname>Xu</surname><order>4</order></author><author><firstname>Yang</firstname><surname>Yang</surname><order>5</order></author><author><firstname>Cheng</firstname><surname>Cheng</surname><orcid>0000-0003-0371-9646</orcid><order>6</order></author></authors><documents><document><filename>68480__33389__f47cb1de3e6d412da30b75cbca86e10a.pdf</filename><originalFilename>68480.AAM.pdf</originalFilename><uploaded>2025-01-21T11:03:32.6277394</uploaded><type>Output</type><contentLength>3281800</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><documentNotes>Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/deed.en</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-03-18T15:48:39.3103735 v2 68480 2024-12-06 A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2024-12-06 MACS Airport delay prediction plays a crucial role in air traffic management practices, including rerouting aircraft, implementing ground delays, and sequencing arrivals. This task is challenging due to the inherent nonlinear characteristics in traffic evolution. The popular deep learning-based traffic prediction methods lack the in-depth exploration of traffic evolution features. In this work, we propose a novel three-channel Temporal Convolution Network (TCN) framework incorporating temporal complex network feature information for airport delay prediction. The complex network feature sequence of the temporal networks can effectively capture the nonlinear dynamic behavior of airport delays. Firstly, we divide the original time series of airport delays into three distinct series (current, daily, and weekly) and feed them into three channels of our model. This operation allows the model to effectively capture the inherent characteristics of local proximity and global periodicity in airport delay time series. Secondly, the complex networks are converted from airport delay time series using complex network theory, and the topological features of networks are combined with three-chamel TCN, to improve the ability of learning short-term nonlinearity of airport delay evolution. Finally, we incorporate weather condition and flight schedule information as external features to further enhance the prediction accuracy. We perform extensive experiments on a flight dataset at Hartsfield-Jackson Atlanta International Airport (ATL), and the results demonstrate the superiority of our approach compared to existing benchmark methods. Journal Article IEEE Transactions on Vehicular Technology 74 3 3793 3803 IEEE 0018-9545 1939-9359 Delays, Airports, Atmospheric modeling, Predictive models, Time series analysis, Complex networks, Meteorology, Computational modeling, Air traffic control, Accuracy 1 3 2025 2025-03-01 10.1109/tvt.2024.3487895 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required This work is supported by the National Natural Science Foundation of China under Grant No. 71801215, the Key Program of Tianjin Science and Technology Plan under Grant No.21JCZDJC00840 and 21JCZDJC00780, the Fundamental Research Funds for the Central Universities under Grant No. 312202YY02 and 3122018D026, and the Civil Aviation Administration of China Safety Capacity Building Fund under Grant No.SKZ49420220027. 2025-03-18T15:48:39.3103735 2024-12-06T13:10:29.6216149 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Shanmei Li 1 Dengjiang Sun 2 Chao Wang 3 Siying Xu 4 Yang Yang 5 Cheng Cheng 0000-0003-0371-9646 6 68480__33389__f47cb1de3e6d412da30b75cbca86e10a.pdf 68480.AAM.pdf 2025-01-21T11:03:32.6277394 Output 3281800 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information
spellingShingle A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information
Cheng Cheng
title_short A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information
title_full A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information
title_fullStr A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information
title_full_unstemmed A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information
title_sort A New Perspective on Airport Delay Prediction: A Three-channel Temporal Convolution Network with Complex Network Information
author_id_str_mv 11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv 11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author Cheng Cheng
author2 Shanmei Li
Dengjiang Sun
Chao Wang
Siying Xu
Yang Yang
Cheng Cheng
format Journal article
container_title IEEE Transactions on Vehicular Technology
container_volume 74
container_issue 3
container_start_page 3793
publishDate 2025
institution Swansea University
issn 0018-9545
1939-9359
doi_str_mv 10.1109/tvt.2024.3487895
publisher IEEE
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
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description Airport delay prediction plays a crucial role in air traffic management practices, including rerouting aircraft, implementing ground delays, and sequencing arrivals. This task is challenging due to the inherent nonlinear characteristics in traffic evolution. The popular deep learning-based traffic prediction methods lack the in-depth exploration of traffic evolution features. In this work, we propose a novel three-channel Temporal Convolution Network (TCN) framework incorporating temporal complex network feature information for airport delay prediction. The complex network feature sequence of the temporal networks can effectively capture the nonlinear dynamic behavior of airport delays. Firstly, we divide the original time series of airport delays into three distinct series (current, daily, and weekly) and feed them into three channels of our model. This operation allows the model to effectively capture the inherent characteristics of local proximity and global periodicity in airport delay time series. Secondly, the complex networks are converted from airport delay time series using complex network theory, and the topological features of networks are combined with three-chamel TCN, to improve the ability of learning short-term nonlinearity of airport delay evolution. Finally, we incorporate weather condition and flight schedule information as external features to further enhance the prediction accuracy. We perform extensive experiments on a flight dataset at Hartsfield-Jackson Atlanta International Airport (ATL), and the results demonstrate the superiority of our approach compared to existing benchmark methods.
published_date 2025-03-01T08:20:29Z
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