Taxi-out time prediction of departure flights based on Stacking and SHAP

Taxi-out time prediction of departure flights based on Stacking and SHAP | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Taxi-out time prediction of departure flights based on Stacking and SHAP Tao Wu, Yanfeng Mao, Junchuan Huang, Xianlin Zeng, Jiangtao Ma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7752315/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract To address the limitations of weak interpretability and poor generalization in existing taxi-out time prediction models, this study proposes a novel prediction model for departing flights based on Stacking ensemble learning and Shapley additive explanations. Firstly, decomposing taxi-out time into unimpeded taxi-out time and dynamic taxi-out time, followed by separate correlation analysis with influencing factors. Then, constructing a Stacking-based prediction model with comparative evaluation between holistic and phased prediction approaches. Finally, implementing SHAP analysis to quantify feature importance, and validate the rationality of the model using actual operating data from Shenzhen Bao'an international airport of China. The results indicate that: (1) Unimpeded taxi-out time is mainly influenced by the configuration of the airport, while the dynamic taxi-out time is mainly influenced by surface traffic flow; (2) Phased prediction shows enhanced interpretability despite marginally inferior performance (MAPE:10.6%, MAE:99.7s, RMSE:140.5s) compared to holistic prediction; (3) The Stacking model achieves superior accuracy (± 60s/±180s/±300s prediction rates: 41.0%/86.3%/96.5%) and generalization capability over existing methods; (4) The dual feature selection mechanism based on Shapley analysis and correlation analysis can ensure high prediction accuracy of the model while effectively reducing feature dimensions. (5) SHAP analysis was employed to quantify feature impacts on taxi-out time and decode feature interactions, thereby demystifying the model's black-box nature and offering actionable insights for air traffic controllers' decision-making. Physical sciences/Engineering Physical sciences/Mathematics and computing taxi out time interpretability Stacking Shapley additive explanations Full Text Additional Declarations No competing interests reported. Supplementary Files BasicData.xlsx Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Nov, 2025 Reviews received at journal 01 Nov, 2025 Reviews received at journal 26 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor invited by journal 07 Oct, 2025 Editor assigned by journal 06 Oct, 2025 Submission checks completed at journal 04 Oct, 2025 First submitted to journal 30 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7752315","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531867877,"identity":"c7baa208-7014-4a72-bd58-a71254f44472","order_by":0,"name":"Tao Wu","email":"","orcid":"","institution":"China Academy of Civil Aviation Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Wu","suffix":""},{"id":531867878,"identity":"3e4891d6-de62-4608-8aa4-a130332a9ca1","order_by":1,"name":"Yanfeng Mao","email":"","orcid":"","institution":"China Academy of Civil Aviation Science and 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SHAP","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"taxi out time, interpretability, Stacking, Shapley additive explanations","lastPublishedDoi":"10.21203/rs.3.rs-7752315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7752315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the limitations of weak interpretability and poor generalization in existing taxi-out time prediction models, this study proposes a novel prediction model for departing flights based on Stacking ensemble learning and Shapley additive explanations. Firstly, decomposing taxi-out time into unimpeded taxi-out time and dynamic taxi-out time, followed by separate correlation analysis with influencing factors. Then, constructing a Stacking-based prediction model with comparative evaluation between holistic and phased prediction approaches. Finally, implementing SHAP analysis to quantify feature importance, and validate the rationality of the model using actual operating data from Shenzhen Bao'an international airport of China. The results indicate that: (1) Unimpeded taxi-out time is mainly influenced by the configuration of the airport, while the dynamic taxi-out time is mainly influenced by surface traffic flow; (2) Phased prediction shows enhanced interpretability despite marginally inferior performance (MAPE:10.6%, MAE:99.7s, RMSE:140.5s) compared to holistic prediction; (3) The Stacking model achieves superior accuracy (\u0026plusmn;\u0026thinsp;60s/\u0026plusmn;180s/\u0026plusmn;300s prediction rates: 41.0%/86.3%/96.5%) and generalization capability over existing methods; (4) The dual feature selection mechanism based on Shapley analysis and correlation analysis can ensure high prediction accuracy of the model while effectively reducing feature dimensions. (5) SHAP analysis was employed to quantify feature impacts on taxi-out time and decode feature interactions, thereby demystifying the model's black-box nature and offering actionable insights for air traffic controllers' decision-making.\u003c/p\u003e","manuscriptTitle":"Taxi-out time prediction of departure flights based on Stacking and SHAP","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 10:05:39","doi":"10.21203/rs.3.rs-7752315/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-07T09:22:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-01T11:35:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T19:53:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T06:41:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283199917082979158642085273072547381660","date":"2025-10-13T01:01:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249183127039473319172016919610920503255","date":"2025-10-11T15:13:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235976033402246134251698192309576097176","date":"2025-10-08T09:03:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T05:57:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T16:37:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T12:15:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-04T06:04:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-30T13:54:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a75ddfe5-81ed-4815-a2bd-02d121f09776","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56542440,"name":"Physical sciences/Engineering"},{"id":56542441,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-23T16:11:30+00:00","versionOfRecord":{"articleIdentity":"rs-7752315","link":"https://doi.org/10.1038/s41598-026-40898-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-20 15:58:03","publishedOnDateReadable":"February 20th, 2026"},"versionCreatedAt":"2025-10-20 10:05:39","video":"","vorDoi":"10.1038/s41598-026-40898-5","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40898-5","workflowStages":[]},"version":"v1","identity":"rs-7752315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7752315","identity":"rs-7752315","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}