Learning the complexity of urban mobility with deep generative collaboration network

Learning the complexity of urban mobility with deep generative collaboration network | 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 Learning the complexity of urban mobility with deep generative collaboration network Yong Li, Yuan Yuan, Jingtao Ding, Depeng Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3666762/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 City-scale individual movements, resulting population flows, and urban morphology intricately intertwine, collectively contributing to the complexity of urban mobility, impacting critical aspects of a city, including socioeconomic exchanges and epidemic transmission. Existing models, derived from the fundamental laws governing human mobility, often capture only partial facets of this complexity. This paper introduces DeepMobility, a powerful deep generative collaboration network to bridge the heterogeneous behaviors of individuals and collective behaviors emerging from the entire population via constructing a unified model that encapsulates the multifaceted nature of complex urban mobility. Our experiments, conducted on mobility trajectories and flows in cities of China and Senegal, reveal that, in contrast to state-of-the-art deep learning models that simply “memorize” observed data, DeepMobility excels in learning the intricate data distribution and successfully reproduces the existing universal scaling laws that characterize human mobility behaviors at both the individual and population levels. DeepMobility also exhibits robust generalization capabilities, enabling it to generate realistic trajectories and flows for cities lacking corresponding training data. Our approach underscores the feasibility of employing generative deep learning to model the underlying mechanism of human mobility, and establishes an effective generative machine learning framework to capture the complexity of urban mobility comprehensively. Physical sciences/Mathematics and computing Scientific community and society/Geography Earth and environmental sciences/Social sciences Full Text Additional Declarations There is NO Competing Interest. Supplementary Files NMISI.pdf 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. 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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-3666762","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":258540759,"identity":"29645632-55ca-4e51-bc5b-72f1e4020c0f","order_by":0,"name":"Yong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACPmYwJVHfDyQPgBGIi08LG0SLDePMNqK1QKg0xg3HwAxitLDzGD4u+HWY2fh++8PDBb/uJPY3MB+8zcNgl4fbYTzGxjP7DrOZHeMxODyz71nijANsydY8DMnFeLSYSfP2HOYBamE4DGQkNhwAivAwHEhsIKBFwriN/QFYy/wD/N8Ia+H5kWZgwMZgcJjnx+HEDQd42AhoYSs25m2wSZA4lmNwmLfhsPHGw2zGlnMMknFq4ec/vPExzx+JBP7m448/8/w5LDvvePPDG28q7HBqYWDgMGBgbIOywQxw5BrgVA8E7A8YGP7AOH9wKhsFo2AUjIIRDAA3LVdeA8nbzAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5617-1659","institution":"Tsinghua University","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Li","suffix":""},{"id":258540760,"identity":"e3c8d228-a00e-46aa-a68f-06d60f2938a3","order_by":1,"name":"Yuan Yuan","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Yuan","suffix":""},{"id":258540761,"identity":"6f783eb0-2ea1-4c12-945d-38e85d666c77","order_by":2,"name":"Jingtao Ding","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Jingtao","middleName":"","lastName":"Ding","suffix":""},{"id":258540762,"identity":"048eb837-b5fc-4417-888c-8d36ad7f5a80","order_by":3,"name":"Depeng Jin","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Depeng","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2023-11-26 09:20:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3666762/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3666762/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50332663,"identity":"caf04646-a533-40cc-a553-0c709337d62d","added_by":"auto","created_at":"2024-01-29 22:29:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8035606,"visible":true,"origin":"","legend":"","description":"","filename":"NMIMobility15.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3666762/v1_covered_4ee87d88-bca3-4561-849c-8bfffd09d36f.pdf"},{"id":48077406,"identity":"a2baa164-6498-4a16-a302-1697e6f393ef","added_by":"auto","created_at":"2023-12-12 16:21:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4620081,"visible":true,"origin":"","legend":"","description":"","filename":"NMISI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3666762/v1/e5a797dc5a7386ec21124ae0.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Learning the complexity of urban mobility with deep generative collaboration network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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-3666762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3666762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"City-scale individual movements, resulting population flows, and urban morphology intricately intertwine, collectively contributing to the complexity of urban mobility, impacting critical aspects of a city, including socioeconomic exchanges and epidemic transmission. 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