Graph-structured gravity model enhances transferable pedestrian flow prediction

Graph-structured gravity model enhances transferable pedestrian 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 Graph-structured gravity model enhances transferable pedestrian flow prediction Meead Saberi, Fatemeh Nourmohammadi, Taha Hossein Rashidi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7414543/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 Understanding and modeling pedestrian flow patterns within cities is essential for promoting public health, social equity, and environmental sustainability. However, pedestrian mobility data are often sparse, imbalanced, or unreliable, particularly in low-resource contexts, forcing planners to transfer models developed in one city to others, often with poor outcomes. This challenge calls for models that are generalizable and robust to pedestrian mobility data limitations. In this study, we introduce a hybrid graph-based method for constructing behaviorally meaningful destination choice sets, coupled with a Transferable Graph Laplacian Gravity Network (TL-GNet), a learning-enhanced gravity model that integrates graph topology and spatial regularization to estimate pedestrian flows. Evaluated using household travel survey data from four cities of Melbourne and Brisbane (Australia), and Seattle and Chicago (USA), our model consistently outperforms traditional and deep gravity models, particularly in predicting low-volume pedestrian flows. TL-GNet demonstrates strong spatial transferability, achieving significantly higher predictive accuracy across cities without retraining. The results highlight the potential of graph-informed models to support pedestrian planning in data-scarce urban environments and advance transferable approaches to modeling active and sustainable mobility. Physical sciences/Physics/Applied physics Physical sciences/Engineering/Civil engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SIFatemehTLGNetpedestrianmobility.pdf Supplementary Information 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-7414543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":505867009,"identity":"b5472527-b151-4117-95e4-b47ede867aff","order_by":0,"name":"Meead Saberi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYLACHiA2AOIDH4AEGzspWg7OAGlhJkULM4jBQEiLbnvzwQ9vGOrkzdl7Hx62+bVNno+ZgfHDxxzcWszOHEuWnMNw2HBnz3GDw7l9tw3bmBmYJWduw6PlRo6BNA/DAcYNN9IYDuf23GYEamFj5sWn5f77z795GOrsN9x/xnDYsue2PWEtN3jYgLYwJ264wcZwmOHH7UTCWs6kmVnOMTicvLMnjeFgb8Pt5DZmxmb8fjl++PGNNxV1ttvZjzF/+PHntu18UBh+xKMFAgygNGMbmGwgpB4Z/CFF8SgYBaNgFIwUAADorFJeKfVACwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6526-239X","institution":"University of New South Wales","correspondingAuthor":true,"prefix":"","firstName":"Meead","middleName":"","lastName":"Saberi","suffix":""},{"id":505867010,"identity":"5382905d-56ba-42be-82f7-e85fcc6db213","order_by":1,"name":"Fatemeh Nourmohammadi","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Nourmohammadi","suffix":""},{"id":505867011,"identity":"764006c2-1c89-4ab3-84c3-39391184a5f2","order_by":2,"name":"Taha Hossein Rashidi","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Taha","middleName":"Hossein","lastName":"Rashidi","suffix":""}],"badges":[],"createdAt":"2025-08-20 07:25:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7414543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7414543/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94123606,"identity":"abd69370-8757-4eed-ba73-f9c1501d0594","added_by":"auto","created_at":"2025-10-22 15:39:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":30018430,"visible":true,"origin":"","legend":"Article File","description":"","filename":"FatemehTLGNetpedestrianmobility.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7414543/v1_covered_19acf1fa-cd23-4c75-87cb-fec056cef4b9.pdf"},{"id":90146890,"identity":"f0d56d56-38c0-4e7b-8fe1-f408b8fa3491","added_by":"auto","created_at":"2025-08-29 05:56:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12682976,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SIFatemehTLGNetpedestrianmobility.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7414543/v1/e77de7207d6b1a0fce5ddcbd.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Graph-structured gravity model enhances transferable pedestrian flow prediction","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-7414543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7414543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Understanding and modeling pedestrian flow patterns within cities is essential for promoting public health, social equity, and environmental sustainability. However, pedestrian mobility data are often sparse, imbalanced, or unreliable, particularly in low-resource contexts, forcing planners to transfer models developed in one city to others, often with poor outcomes. This challenge calls for models that are generalizable and robust to pedestrian mobility data limitations. In this study, we introduce a hybrid graph-based method for constructing behaviorally meaningful destination choice sets, coupled with a Transferable Graph Laplacian Gravity Network (TL-GNet), a learning-enhanced gravity model that integrates graph topology and spatial regularization to estimate pedestrian flows. Evaluated using household travel survey data from four cities of Melbourne and Brisbane (Australia), and Seattle and Chicago (USA), our model consistently outperforms traditional and deep gravity models, particularly in predicting low-volume pedestrian flows. TL-GNet demonstrates strong spatial transferability, achieving significantly higher predictive accuracy across cities without retraining. The results highlight the potential of graph-informed models to support pedestrian planning in data-scarce urban environments and advance transferable approaches to modeling active and sustainable mobility.","manuscriptTitle":"Graph-structured gravity model enhances transferable pedestrian flow prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-29 05:56:04","doi":"10.21203/rs.3.rs-7414543/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":"3826b24c-6956-40b6-a049-73a8ee6788af","owner":[],"postedDate":"August 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53732898,"name":"Physical sciences/Physics/Applied physics"},{"id":53732899,"name":"Physical sciences/Engineering/Civil engineering"}],"tags":[],"updatedAt":"2025-10-22T15:31:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-29 05:56:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7414543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7414543","identity":"rs-7414543","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}