Cross-Domain Transformer Spatial-Temporal Fusion Network for Traffic Flow Forecasting

Cross-Domain Transformer Spatial-Temporal Fusion Network for Traffic Flow Forecasting | 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 Cross-Domain Transformer Spatial-Temporal Fusion Network for Traffic Flow Forecasting Yijun Xiong, Kai Xu, Mo Chen, Haifeng Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6101035/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Accurate traffic forecasting is challenging due to the intricate inter-dependencies of road networks and congestion caused by unexpected accidents. Recent work has focused on dynamically changing traffic characteristics but has paid less attention to the global cross-spatial-temporal domain of modeling, which may limit their performance. In this paper, we propose a novel plug-and-play fusion unit to accurately express the spatial-temporal dependencies by cross-domain complementary information integration, named the Cross-Domain Transformer Spatial-Temporal Fusion Network (CDTSTFN). By introducing two-stage fusion units, we compensate information loss and resolve the mismatch in fused information. This enables CDTSTFN to largely augment the base spatial-temporal predictors with learned both local-global spatial and short-long temporal dependencies on cross-domain spatial-temporal patterns. A comprehensive set of both quantitative and qualitative assessments is performed on six public traffic network datasets (PeMS03, PeMS04, PeMS07, PeMS08, METR-LA, and PeMS-BAY), demonstrating the superior performance of our model. Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 May, 2025 Reviews received at journal 06 May, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers invited by journal 25 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 22 Apr, 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. 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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-6101035","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449416609,"identity":"b57889d5-9183-4b8a-b26d-a0217ca5ecb3","order_by":0,"name":"Yijun Xiong","email":"","orcid":"","institution":"Chengdu University","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Xiong","suffix":""},{"id":449416610,"identity":"4fde872b-bcbd-49ab-8e0a-9f2449434313","order_by":1,"name":"Kai Xu","email":"","orcid":"","institution":"Chengdu University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Xu","suffix":""},{"id":449416611,"identity":"5a81cb3e-0449-4b64-8afe-d0ba1fe45743","order_by":2,"name":"Mo Chen","email":"","orcid":"","institution":"Chengdu University","correspondingAuthor":false,"prefix":"","firstName":"Mo","middleName":"","lastName":"Chen","suffix":""},{"id":449416612,"identity":"61c55e05-aeae-4088-8f54-83b154b3df0d","order_by":3,"name":"Haifeng Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACAwnGB0DKBogPgPjMxGhhNgBSaUB8mDQth2GqidBiLt3M+Ljg13l7g4Pnj0kwVFgnNrCfPYBXi+Wcw8zGM/tuJ244cJhNguFMemIDT14CfofdyD8mzdtzO8EMpIWx7XBigwSPAQEtyey/eXvO2UO0/CNOCxszz48DjNvAWhqI08IszduQnLj/wGFji4Rj6cZtPDkEtTB+5vljZy854+DDGx9qrGX72c/g1wIGjG1AQuIAA0MCkGYjrB4E/gAxfwNxakfBKBgFo2DkAQBlU0gRb9BpKwAAAABJRU5ErkJggg==","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-02-25 03:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6101035/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6101035/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-06586-6","type":"published","date":"2025-07-02T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86178934,"identity":"59edb471-a5b3-423a-ba03-0c0eaa5c5a6e","added_by":"auto","created_at":"2025-07-07 16:11:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11506895,"visible":true,"origin":"","legend":"","description":"","filename":"revision.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6101035/v1_covered_6a5837f1-0f51-4425-840e-ebbcc2b8bb82.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cross-Domain Transformer Spatial-Temporal Fusion Network for Traffic Flow Forecasting","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6101035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6101035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate traffic forecasting is challenging due to the intricate inter-dependencies of road networks and congestion caused by unexpected accidents. 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