A Passenger Flow Prediction Model Based on Graph Convolutional Network with Multivariate Spatio-temporal Correlation

A Passenger Flow Prediction Model Based on Graph Convolutional Network with Multivariate Spatio-temporal Correlation | 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 A Passenger Flow Prediction Model Based on Graph Convolutional Network with Multivariate Spatio-temporal Correlation Ying Ma, Yang LI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4471720/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Accurate prediction of short-term passenger flow is very important for rational planning and stable operation of cities, however, the problem of passenger flow prediction faces many challenges, including both the establishment of an effective spatio-temporal dynamic model structure and the necessity to comprehensively consider a variety of factors affecting the explicit and implicit passenger flow. So, a Multi-Variate Spatio-Temporal Correlation Graph Convolutional Network model (MVSTCGCN) is proposed. The model utilizes three kinds of spatially correlated graphs to construct a base graph, which is combined to capture spatio-temporal features globally; temporal attention mechanism, spatial attention mechanism, graph convolution operation, and spatio-temporal convolution constitute the spatio-temporal graph convolution module to capture local spatio-temporal features; meanwhile, the core module of graph convolution network is improved by being integrated wavelet transformation operators. The model is validated by New York taxi YellowTrip dataset and self-built dataset respectively; the simulation experiments show that the performance of our algorithm has more obvious advantages compared with other excellent algorithms. Short-term passenger flow prediction Graph convolution network Wavelet transformation Temporal attention mechanisms Spatial attention mechanisms Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 May, 2024 Reviewers invited by journal 24 May, 2024 First submitted to journal 28 Jan, 2024 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-4471720","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":306309566,"identity":"ef304b5a-76a7-4781-a0e9-53c260f9e124","order_by":0,"name":"Ying Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACNjiLvbHx4QcStBgwMPAcbjaWIMEyoBaJ9DYBHmLU8kn3HnzMU/NHznzmwzYGCQY7Od0GQg6TOZdszHPMwFjmdmLbgwKGZGOzA4S0SOSYSc5gM0icIZ3YbiDBcCBxG3Fa/gG1SB5sk+AhVovExzagFglG4rUYG3zsMzaW4EkEBrIBEX6Rn5Fj+CDhm5ycBPvxhw8/VNjJEdSCBgxIUz4KRsEoGAWjAAcAAE9dOQ5LDdCVAAAAAElFTkSuQmCC","orcid":"","institution":"Xi'an Technological University","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Ma","suffix":""},{"id":306309567,"identity":"148da961-3186-44cf-a14f-138be7d980b5","order_by":1,"name":"Yang LI","email":"","orcid":"","institution":"Xi'an Technological University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"LI","suffix":""}],"badges":[],"createdAt":"2024-05-24 10:03:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4471720/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4471720/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57842485,"identity":"258d6f43-7c47-4cb4-8e53-c9da25de25cb","added_by":"auto","created_at":"2024-06-06 10:12:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":797850,"visible":true,"origin":"","legend":"","description":"","filename":"MAYing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4471720/v1_covered_d4cc3cc4-9f43-42bd-8b58-96c038bc6909.pdf"}],"financialInterests":"","formattedTitle":"A Passenger Flow Prediction Model Based on Graph Convolutional Network with Multivariate Spatio-temporal Correlation","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":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Short-term passenger flow prediction, Graph convolution network, Wavelet transformation, Temporal attention mechanisms, Spatial attention mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-4471720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4471720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate prediction of short-term passenger flow is very important for rational planning and stable operation of cities, however, the problem of passenger flow prediction faces many challenges, including both the establishment of an effective spatio-temporal dynamic model structure and the necessity to comprehensively consider a variety of factors affecting the explicit and implicit passenger flow. 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