Identifying individual anchoring regions by mining public transport smart card data

Identifying individual anchoring regions by mining public transport smart card 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 Research Article Identifying individual anchoring regions by mining public transport smart card data Megan Born, Mark Reynolds, Rachel Cardell-Oliver This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7726441/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 locations significant to an individual’s routine travels – such as home and work – gives important context to the individual’s travel patterns. Termed 'anchoring points', these represent places where activities are repeated in space and time around which other activities are arranged. While data from public transport automated fare collection (AFC) systems provides a structured and detailed view of movement patterns with minimal data collection efforts, it cannot provide the same level of background detail of these locations that can be achieved with more labour-intensive data collection methods such as user travel surveys. Where previous studies have developed methods based on AFC data to identify these anchoring points for each individual as points, this work considers these locations as spatial regions 'anchoring regions', acknowledging that individuals may have multiple transport options available to them to access the same activities. Further, this work also develops an approach for allowing individuals to have a flexible number of these anchoring regions, recognising that individual routines and circumstances may differ greatly. Using temporal patterns (such as time of day and duration of time spent at the location) to identify likely activity labels and then the distribution of these activities within each region, a Gaussian mixture model is used to characterise these regions. This resulted in five region activity clusters, with the activity distributions compared to land use and the final clusters of type: 'residences', 'workplaces', 'education', 'residences/leisure', 'workplaces/leisure' . While a sample of the full dataset was used for clustering, the stability of the resulting region activity clusters was tested with a newly developed methodology to validate the results across sample sizes. Smart-card data Automated fare collection Public transportation Full Text Additional Declarations The authors declare no competing interests. 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-7726441","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521404304,"identity":"3826b8c1-1593-4373-83f0-263a68e9d922","order_by":0,"name":"Megan Born","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8412-7321","institution":"University of Western Australia","correspondingAuthor":true,"prefix":"","firstName":"Megan","middleName":"","lastName":"Born","suffix":""},{"id":521404305,"identity":"e5a1c81d-e970-47a1-9010-fb6a731c522f","order_by":1,"name":"Mark Reynolds","email":"","orcid":"https://orcid.org/0000-0002-5415-0544","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Reynolds","suffix":""},{"id":521404306,"identity":"aee6e9a8-aabc-487c-84b9-fa5796663518","order_by":2,"name":"Rachel Cardell-Oliver","email":"","orcid":"https://orcid.org/0000-0003-0590-1003","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Cardell-Oliver","suffix":""}],"badges":[],"createdAt":"2025-09-27 06:50:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7726441/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7726441/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92537398,"identity":"bc9f848e-d19c-4934-915e-1a99aa4454fa","added_by":"auto","created_at":"2025-09-30 17:42:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991277,"visible":true,"origin":"","legend":"","description":"","filename":"NCAAspecialissuepaperfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7726441/v1_covered_e58c837d-ca28-4a36-aa26-9a35d1648644.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIdentifying individual anchoring regions by mining public transport smart card data\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Western Australia","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":"Smart-card data, Automated fare collection, Public transportation","lastPublishedDoi":"10.21203/rs.3.rs-7726441/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7726441/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding locations significant to an individual’s routine travels – such as home and work – gives important context to the individual’s travel patterns. 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