{"paper_id":"2d09dc48-2bd7-441e-914d-77eac91f28ca","body_text":"Application of Attention Fusion Dynamic Graph Convolutional Networks Enhanced with Hub Nodes in 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 Research Article Application of Attention Fusion Dynamic Graph Convolutional Networks Enhanced with Hub Nodes in Traffic Flow Forecasting Chengchun Yang, Tianrui Li, Haonan Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8835372/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Real-time and precise traffic flow prediction serves as a vital enabler for modern intelligent transportation systems. In this domain, graph neural networks demonstrated remarkable proficiency in capturing the intricate spatiotemporal relationships embedded within traffic data. However, we identify a critical limitation in existing attention-based dynamic GNNs: their inadequate modeling of hub nodes, which maintain extensive connections and exhibit more intricate spatiotemporal patterns than ordinary nodes in transportation networks.To address this gap, we propose the Hub Node-enhanced Attention Fusion Dynamic Graph Convolutional Network (HN-AT-DGCN), a novel encoder-decoder framework comprising four essential components. The multi-scale feature fusion module first extracts traffic flow characteristics across varied temporal scales. The encoder then employs a dynamic graph convolutional gated recurrent unit to capture comprehensive spatiotemporal dependencies. A multi-head temporal attention mechanism further models long-range temporal patterns, while a 2D graph convolutional decoder ultimately generates future traffic flow predictions.Our key contributions are twofold. We introduce a hub node identification module that automatically detects critical nodes in the network. Our methodology incorporates a novel dynamic graph convolution scheme that facilitates discriminative feature learning across three semantic levels: node-wise properties, structural correlations, and hub-dominated propagations, comprehensively modeling multi-faceted relationships in traffic networks.The effectiveness of our method is evidenced by thorough evaluations across three authentic traffic datasets, where it attains leading performance and exceeds 15 baseline approaches. Traffic forecasting graph neural networks hub nodes attention mechanisms Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 09 Feb, 2026 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. 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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-8835372\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":617538966,\"identity\":\"c0e42a64-375f-4b26-ad45-8722f6a7f4b1\",\"order_by\":0,\"name\":\"Chengchun Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Southwest Jiaotong University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chengchun\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"},{\"id\":617538967,\"identity\":\"7248a562-4d10-4368-80ac-e2f2fbf1c361\",\"order_by\":1,\"name\":\"Tianrui Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Southwest Jiaotong University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tianrui\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":617538969,\"identity\":\"6ae7c1b4-e1d4-49e2-b604-6ea26e435163\",\"order_by\":2,\"name\":\"Haonan Luo\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYBACPgkGxgcMDGxgjgRRWtgkGJgNwBQpWsDKSdEi3Xus8mcbX53BAeaDt3kY7PIIa5E5l3abt41NwuAAW7I1D0NyMREOyzG7zbgNpIXHTJqH4UBiAzFaCn+CtfB/I14LAy/EFjaitRhL8/5jk5x5mM3Yco5BMmEt/BI5hh9/nDnGz3e8+eGNNxV2hLVAwTEGBmYQbUCkeiCoIV7pKBgFo2AUjDwAAKsAMHdg+KEcAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Southwest Jiaotong University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Haonan\",\"middleName\":\"\",\"lastName\":\"Luo\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-10 02:09:33\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8835372/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8835372/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":106723909,\"identity\":\"9d962c9c-a5b2-46e6-8d0b-5676096ac036\",\"added_by\":\"auto\",\"created_at\":\"2026-04-12 18:20:23\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1285963,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"HNATDGCN3.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8835372/v1_covered_7057ddad-73f4-4b17-b5e0-7f944813bd99.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Application of Attention Fusion Dynamic Graph Convolutional Networks Enhanced with Hub Nodes in Traffic Flow Forecasting\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"international-journal-of-machine-learning-and-cybernetics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jmlc\",\"sideBox\":\"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)\",\"snPcode\":\"13042\",\"submissionUrl\":\"https://submission.nature.com/new-submission/13042/3\",\"title\":\"International Journal of Machine Learning and Cybernetics\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Traffic forecasting, graph neural networks, hub nodes, attention mechanisms\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8835372/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8835372/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eReal-time and precise traffic flow prediction serves as a vital enabler for modern intelligent transportation systems. 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