Abnormal Driving Pattern Detection from GPS Trajectories Using Vision Transformer

Abnormal Driving Pattern Detection from GPS Trajectories Using Vision Transformer | 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 Abnormal Driving Pattern Detection from GPS Trajectories Using Vision Transformer Seyedeh Gol Ara Ghoreishi, Kwangsoo Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8653475/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 Given GPS points on a transportation network, the Driving Pattern Detection (DPD) problem aims to classify drivers as normal or abnormal based on their driving behavior. The DPD problem is challenging due to the variability in trip lengths, routes, and spatial patterns, which complicates input standardization for deep learning models. In this paper, we introduce a novel spatial representation learning framework for the DPD problem by analyzing driving patterns using a real-world dataset. We propose using binary grid images to capture the spatial structure of driving trajectories and present a new driving behavior representation for input to a Vision Transformer (ViT) model for driver classification. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an F1 score of 94% that significantly outperforms the baseline models. The results indicate that binary grid representations can effectively encode interpretable spatial patterns in driving behavior, with direct relevance to improved driver classification, road safety, and cognitive health assessment. Spatiotemporal data GPS data trajectory analysis driving behavior older driver classification vision transformer attention mechanism deep learning 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-8653475","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585420788,"identity":"2297cab8-c9b9-432b-957e-b2ddc39427bd","order_by":0,"name":"Seyedeh Gol Ara Ghoreishi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3OMUvEMBTA8RcCvSXS9RXBfoVXCqeD2K+SUrjZSW6QI3Jwk3BrB/0Iwq1uOQLnEq5r4BaLq0p3D7GdHMSco0P+Q4aX/MgDCIX+ZWI4EIhzrTs6j7+Hh0lUtvXlJFF/JAAEIs9FZw6TdPmwfplOz4rTEYxRUINxLdnz2+J3Qu69yqzF8nEOE0TaITrJs3sfQTtObhYoycAGiXYzcDI6PvKQtB7IJxZk2AC3mDo5+vARaG57opCtDOekSSP1v3AfISfyTG2wXJmItYoqzGw7T+62nsWWNmvV9aygpunMfn+BJ0/Vunu98iyG8seIKc/7vlj770OhUCgEXwWbUsK202bIAAAAAElFTkSuQmCC","orcid":"","institution":"Florida Atlantic University","correspondingAuthor":true,"prefix":"","firstName":"Seyedeh","middleName":"Gol Ara","lastName":"Ghoreishi","suffix":""},{"id":585420793,"identity":"f37a380e-1280-4266-b5a0-b25635397b28","order_by":1,"name":"Kwangsoo Yang","email":"","orcid":"","institution":"Florida Atlantic University","correspondingAuthor":false,"prefix":"","firstName":"Kwangsoo","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-01-20 22:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8653475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8653475/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103605387,"identity":"f8cb8f0a-143f-4a8a-b9fb-be4154d5cda5","added_by":"auto","created_at":"2026-02-27 14:42:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":717420,"visible":true,"origin":"","legend":"","description":"","filename":"AbnormalDrivingPatternDetectionUsingVisionTransformer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8653475/v1_covered_3184194f-869d-4169-9488-5898cf4c5319.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abnormal Driving Pattern Detection from GPS Trajectories Using Vision Transformer","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":"Spatiotemporal data, GPS data, trajectory analysis, driving behavior, older driver classification, vision transformer, attention mechanism, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-8653475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8653475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGiven GPS points on a transportation network, the Driving Pattern Detection (DPD) problem aims to classify drivers as normal or abnormal based on their driving behavior. 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