Short-term passenger flow prediction for urban rail systems: A deep learning approach utilizing multi-source big data

Short-term passenger flow prediction for urban rail systems: A deep learning approach utilizing multi-source big data | 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 Method Article Short-term passenger flow prediction for urban rail systems: A deep learning approach utilizing multi-source big data Hongmeng Cui, Bingfeng Si, Dazhuang Chi, Yueqing Li, Ge Li, Yuanmeng Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4665739/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 Predicting short-term passenger flow in urban rail transit is crucial for intelligent and real-time management of urban rail systems. This study utilizes deep learning techniques and multi-source big data to develop an enhanced spatial-temporal long short-term memory (ST-LSTM) model for forecasting subway passenger flow. The model includes three key components: (1) a temporal correlation learning module that captures travel patterns across stations, aiding in the selection of effective training data; (2) a spatial correlation learning module that extracts spatial correlations between stations using geographic information and passenger flow variations, providing an interpretable method for quantifying these correlations; and (3) a fusion module that integrates historical spatial-temporal features with real-time data to accurately predict passenger flow. Additionally, we discuss the model's interpretability. The ST-LSTM model is evaluated with two large-scale real-world subway datasets from Nanjing and Beijing. Experimental results show that the ST-LSTM model effectively captures spatial-temporal correlations and significantly outperforms other benchmark methods. Passenger flow prediction Urban rail transit Deep learning Spatial-temporal correlations 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-4665739","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":329751759,"identity":"568bdf41-2c0c-45a0-a539-2f96d3c81954","order_by":0,"name":"Hongmeng Cui","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hongmeng","middleName":"","lastName":"Cui","suffix":""},{"id":329751760,"identity":"8efec2db-516c-4535-b4c3-66ed1b49d8fc","order_by":1,"name":"Bingfeng Si","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACA+YDDAc+NrCBORLEaWFLYDg4s4FNgjQtzLwNDCRoMWdjf3jYdgdfncEB5oO3eRjs8ghqsWzjMTice4ZNwuAAW7I1D0NyMWGH3e9hOJzbBtLCYybNw3AgsYGglmPsDw5bgrXwfyNWC4PBYUaILWzEaQH55WDvGTbJmYfZjC3nGCQT1gIMsccffu44xs93vPnhjTcVdoS1QMExBgZmsDuJVA8ENcQrHQWjYBSMgpEHAMbqOS2TfhGjAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Bingfeng","middleName":"","lastName":"Si","suffix":""},{"id":329751761,"identity":"a0fbdcdb-450c-4339-a59c-84760d7ea3b1","order_by":2,"name":"Dazhuang Chi","email":"","orcid":"","institution":"Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Dazhuang","middleName":"","lastName":"Chi","suffix":""},{"id":329751762,"identity":"4fbfb27f-1725-4891-888e-b2824206faaa","order_by":3,"name":"Yueqing Li","email":"","orcid":"","institution":"Xi'an Railway Vocational \\\u0026 Technical Institute","correspondingAuthor":false,"prefix":"","firstName":"Yueqing","middleName":"","lastName":"Li","suffix":""},{"id":329751763,"identity":"62d89d8e-6b0e-4357-83ee-724b9594ce4f","order_by":4,"name":"Ge Li","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Ge","middleName":"","lastName":"Li","suffix":""},{"id":329751764,"identity":"6aab7e25-08fb-48ae-acf6-482bbb76aaa1","order_by":5,"name":"Yuanmeng Chen","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuanmeng","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-07-01 06:29:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4665739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4665739/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63310238,"identity":"4a13b31d-0d2b-4d32-a116-bb199f42ab73","added_by":"auto","created_at":"2024-08-26 19:30:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4019260,"visible":true,"origin":"","legend":"","description":"","filename":"Adeeplearningmethodbasedonbigdatatopredictpassengerflow.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4665739/v1_covered_418568fc-fe7e-48e9-b49e-9c70d7626cd1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-term passenger flow prediction for urban rail systems: A deep learning approach utilizing multi-source big data","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":"Passenger flow prediction, Urban rail transit, Deep learning, Spatial-temporal correlations","lastPublishedDoi":"10.21203/rs.3.rs-4665739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4665739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Predicting short-term passenger flow in urban rail transit is crucial for intelligent and real-time management of urban rail systems. 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