Improving Network Level Pedestrian Activity Prediction by Accounting for Spatial and Longitudinal Clustering in Crowdsourced Data

Improving Network Level Pedestrian Activity Prediction by Accounting for Spatial and Longitudinal Clustering in Crowdsourced 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 Article Improving Network Level Pedestrian Activity Prediction by Accounting for Spatial and Longitudinal Clustering in Crowdsourced Data Parsa Soleyman Farahani, Aditi Misra, Shubhayan Ukil, Krista Nordback, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8715658/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Crowdsourced data are increasingly used to estimate active transportation volumes across large networks. Although bicycling data are widely available from third-party aggregators, comparable pedestrian data have been limited. Until recently, most pedestrian datasets captured only footfall at points of interest. Strava Metro began releasing link-level pedestrian activity data last year, and evaluations of these data remain scarce. This study is among the first to use Strava’s pedestrian activity data and assess their validity against at-location counter measurements. We then develop a network-level pedestrian volume model that integrates Strava data with land use, weather, infrastructure, and facilities variables. We also address a methodological gap in the prevalent models using crowdsourced data, where models often ignore variability in longitudinal and spatially clustered data. Using a mixed-effects predictive framework, we capture this structure and improve predictive performance. Our findings introduce a validated pedestrian data source and a robust model for network-level pedestrian volume estimation. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Apr, 2026 Reviews received at journal 11 Mar, 2026 Reviews received at journal 01 Mar, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 31 Jan, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 27 Jan, 2026 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-8715658","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":591749465,"identity":"446eb551-70d7-4b2d-be3f-4641fc7561af","order_by":0,"name":"Parsa Soleyman Farahani","email":"","orcid":"","institution":"University of Colorado Denver","correspondingAuthor":false,"prefix":"","firstName":"Parsa","middleName":"Soleyman","lastName":"Farahani","suffix":""},{"id":591749466,"identity":"63f6dd52-a240-4121-86eb-7bfdcea1e83c","order_by":1,"name":"Aditi Misra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFCCxAaGBAYGHiCL8QEDAzNMmBmnBqCWxgaoFmYDIrUkMDZAWWwSRGnhb09uf/CAYZuM/OzDz6p5aqzlzCWSj31gqLBObMChReLMQ5DDbvMYnEszu81zLN3YckZa8gyGM+k4tTDcSIRq4WEwu83bcDhxw5kzxgyMbYdxapGHaZHvYf9WDNRSv+HM+c8MjP9wazGAaWE4w2PGDNSSYHC8h5mBsQG3FkOgX2YkGAAddoanWHLOsXTDDcfbjBkSgJ7CpUXuePqDjz8qbtsDHbbxw5saa3mDw8yPGT7UWMvi9D7EeegCCXiVj4JRMApGwSggBABOtFrE/deIFQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Colorado Denver","correspondingAuthor":true,"prefix":"","firstName":"Aditi","middleName":"","lastName":"Misra","suffix":""},{"id":591749467,"identity":"aecf46bc-04fe-4f5d-a4cf-44142b8c9a29","order_by":2,"name":"Shubhayan Ukil","email":"","orcid":"","institution":"University of Michigan–Ann Arbor","correspondingAuthor":false,"prefix":"","firstName":"Shubhayan","middleName":"","lastName":"Ukil","suffix":""},{"id":591749468,"identity":"b8652da8-819f-41ef-bca2-9a4dfeb1f3a8","order_by":3,"name":"Krista Nordback","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Krista","middleName":"","lastName":"Nordback","suffix":""},{"id":591749469,"identity":"47f0691d-ad6a-4a13-8169-b9e1cf39eba7","order_by":4,"name":"Wesley Marshall","email":"","orcid":"","institution":"University of Colorado Denver","correspondingAuthor":false,"prefix":"","firstName":"Wesley","middleName":"","lastName":"Marshall","suffix":""}],"badges":[],"createdAt":"2026-01-28 03:08:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8715658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8715658/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103049405,"identity":"44d6aeb5-e7e7-4972-8cfb-4bb348690f3d","added_by":"auto","created_at":"2026-02-20 07:40:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1108635,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8715658/v1_covered_02825027-9a0a-40b8-b75c-b79e32a43a68.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Network Level Pedestrian Activity Prediction by Accounting for Spatial and Longitudinal Clustering in Crowdsourced Data","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":"npj-sustainable-mobility-and-transport","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Mobility and Transport](https://www.nature.com/npjsustainmobil)","snPcode":"44333","submissionUrl":"https://submission.springernature.com/new-submission/44333/3","title":"npj Sustainable Mobility and Transport","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8715658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8715658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCrowdsourced data are increasingly used to estimate active transportation volumes across large networks. 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