Fusion Mamba with Mixed Graph Learner for Long-term Traffic Flow Prediction

Fusion Mamba with Mixed Graph Learner for Long-term Traffic Flow Prediction | 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 Fusion Mamba with Mixed Graph Learner for Long-term Traffic Flow Prediction Haifeng Huang, Kai Xu, Mo Chen, Yijun Xiong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8046821/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Long-term prediction as a vital task of multivariate traffic flow time series remains challenging due to spatial-temporal heterogeneity and non-stationarity. To address this, we propose Fusion Mamba with Mixed Graph Learner (FMMGL), a novel framework that integrates the graph learners with a cross-fusion Mamba. Specifically, the Mixed Graph Learner reconstructs the traffic data by memorizing typical features in input sequence to explicitly disentangles the heterogeneity in space, and Fusion Mamba further enlarges the receptive field to be robust to temporal dependencies from normal to non-stationarity. Together, these components enable FMMGL to effectively tackle the any traffic scenarios, delivering improved performance with optimized computational efficiency. Comprehensive experiments on four real-world traffic datasets (PeMS03, PeMS04, PeMS07 and PeMS08) demonstrate the superiority of FMMGL outperforms several state-of-the-art methods. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8046821","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":544494545,"identity":"59db66bf-2e8e-4c0f-bdd6-131bd3131695","order_by":0,"name":"Haifeng Huang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Huang","suffix":""},{"id":544494546,"identity":"b54b12eb-f38c-4e84-8ec0-6e1d9d5e6345","order_by":1,"name":"Kai Xu","email":"","orcid":"","institution":"Chengdu University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Xu","suffix":""},{"id":544494547,"identity":"ccd18230-15e6-477d-95fc-779d9c0408a0","order_by":2,"name":"Mo Chen","email":"","orcid":"","institution":"Chengdu University","correspondingAuthor":false,"prefix":"","firstName":"Mo","middleName":"","lastName":"Chen","suffix":""},{"id":544494548,"identity":"59345c1d-9fb5-4919-9ea9-982c06cb514c","order_by":3,"name":"Yijun Xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACZjApAaSYDzDwgNgHiNfClkCkFgTgMSBOi8Fx5mMPv7ZZsBvc7vn44W0bgxzfjQTGzwV4tEg2s6Uby7ZJMBvcObtZcm4bg7HkjQRm6Rl4tPAz85hJS4K03MjdxszbxpC44UYCGzMPHi1sCC05z0Ba6glqAdki+RGihQ2kJcGAkBagX9KkGc5JMEveSDOWnHNOwnDmmYfN0vi0GJw/fEzyR1ldMt+N5Icf3pTZyPMdTz74GZ8WEGDmZWNIhrIlgJixgYAGoJIffxjsCKoaBaNgFIyCkQsAuxRDJtNXZqoAAAAASUVORK5CYII=","orcid":"","institution":"Chengdu University","correspondingAuthor":true,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Xiong","suffix":""}],"badges":[],"createdAt":"2025-11-06 10:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8046821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8046821/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96393256,"identity":"4d96473e-ea88-43f2-b939-2c0300d556da","added_by":"auto","created_at":"2025-11-20 14:50:22","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5306,"visible":true,"origin":"","legend":"","description":"","filename":"1e11f6b961e94d7e938c03871e0468d3.json","url":"https://assets-eu.researchsquare.com/files/rs-8046821/v1/fec556e486d802fff4b6f6d5.json"},{"id":97672119,"identity":"b4e715fc-bfd2-4e0b-a8f5-9d221901daa8","added_by":"auto","created_at":"2025-12-08 09:34:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9298775,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8046821/v1_covered_4a47bb10-1da4-456d-a0dd-b8d4eee2e79a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fusion Mamba with Mixed Graph Learner for Long-term Traffic Flow Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8046821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8046821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Long-term prediction as a vital task of multivariate traffic flow time series remains challenging due to spatial-temporal heterogeneity and non-stationarity. To address this, we propose Fusion Mamba with Mixed Graph Learner (FMMGL), a novel framework that integrates the graph learners with a cross-fusion Mamba. Specifically, the Mixed Graph Learner reconstructs the traffic data by memorizing typical features in input sequence to explicitly disentangles the heterogeneity in space, and Fusion Mamba further enlarges the receptive field to be robust to temporal dependencies from normal to non-stationarity. Together, these components enable FMMGL to effectively tackle the any traffic scenarios, delivering improved performance with optimized computational efficiency. Comprehensive experiments on four real-world traffic datasets (PeMS03, PeMS04, PeMS07 and PeMS08) demonstrate the superiority of FMMGL outperforms several state-of-the-art methods.","manuscriptTitle":"Fusion Mamba with Mixed Graph Learner for Long-term Traffic Flow Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 14:50:17","doi":"10.21203/rs.3.rs-8046821/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fb9ca96-ce87-484e-9610-34bfa7c52ca0","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57932432,"name":"Physical sciences/Engineering"},{"id":57932433,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-05T13:24:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 14:50:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8046821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8046821","identity":"rs-8046821","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}