Advancing Rotation and Scale-Invariant Trajectory Representations for Maritime and Aviation Mobility Data Analysis

Advancing Rotation and Scale-Invariant Trajectory Representations for Maritime and Aviation Mobility Data Analysis | 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 Advancing Rotation and Scale-Invariant Trajectory Representations for Maritime and Aviation Mobility Data Analysis Cristiano Landi, Natalia Andrienko, Gennady Andrienko, Riccardo Guidotti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9056857/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 Spatiotemporal data from connected vehicles, sensors, and GPS-enabled devices increasingly support applications ranging from traffic management to environmental analysis. Whereas earlier studies relied on aggregated flows or coarse-grained movement representations, high-frequency data now enable the analysis of fine-grained individual motion and the geometric properties of trajectories. In movement-constrained settings such as road traffic, geometric detail may be of limited value, but in unconstrained environments such as maritime and aviation mobility data, movement geometry can provide important cues about underlying activities. Despite this potential, geometry-based trajectory analysis remains underexplored and is often constrained by rigid decision rules that limit real-world applicability. This paper introduces RoSITa, a spatiotemporal analytics framework that leverages rotation- and scale-invariant shape signatures derived from a relative Hough transform to cluster and structure subtrajectory patterns for intuitive visual exploration. We demonstrate how RoSITa supports visual inspection to identify similar trajectory segments and show that it outperforms existing approaches on a classification benchmark when the task is driven by movement geometry. Finally, we present two real-world case studies illustrating its ability to capture 2D and 3D motion dynamics, enable interpretable human-in-the-loop analysis, and facilitate the generation of labeled data for future machine learning applications. Spatio-tempral data Knowledge representation and reasoning Human-centered computing 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. 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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-9056857","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609926391,"identity":"b8b9430e-6863-400b-baaf-f0626c644bbb","order_by":0,"name":"Cristiano Landi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABHUlEQVRIiWNgGAWjYFACxgYGBgMQgxnIYJOQY5AAcyUY2MBSOLQcMIDpZZMwRtbSiEMPA8MBuHVsDIkNEC1wIQzAP/tw8+cPBXZ5/EAzPxeUWaRvl27ewMy7wyKPj4G5/QEWLRLnEtskDhgkF0s2MDZLzzgnkbtzzrECZt4zEsW4HGbAw9gG9Atz4oYDjA3SvG0SuRtu5BgwAxmJbbi1NH84YFAP0tL8G6gy3YAILQ1Ahx0GaWkD2ZJAUIvEGcY2iTMGxxNnNjO2WfOckzDcOSOt4ODcNqBfmBkbZ2ALsR72xx8q/lQn9rM3H77NU1Ynby6RvPHB27a6PPn29gcfsGhBAGaYUxkgEZUAFyEIYPGYQKyGUTAKRsEoGPYAANsQYBPcapzoAAAAAElFTkSuQmCC","orcid":"","institution":"University of Pisa","correspondingAuthor":true,"prefix":"","firstName":"Cristiano","middleName":"","lastName":"Landi","suffix":""},{"id":609926392,"identity":"b3e75e04-4d79-47ab-bbe7-10a623512395","order_by":1,"name":"Natalia Andrienko","email":"","orcid":"","institution":"Fraunhofer Institute for Intelligent Analysis and Information Systems","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Andrienko","suffix":""},{"id":609926393,"identity":"6ed5720d-0726-4e43-9c82-08999306a07c","order_by":2,"name":"Gennady Andrienko","email":"","orcid":"","institution":"Fraunhofer Institute for Intelligent Analysis and Information Systems","correspondingAuthor":false,"prefix":"","firstName":"Gennady","middleName":"","lastName":"Andrienko","suffix":""},{"id":609926394,"identity":"46db149e-b77f-46e2-942c-10700246e5ca","order_by":3,"name":"Riccardo Guidotti","email":"","orcid":"","institution":"University of Pisa","correspondingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Guidotti","suffix":""}],"badges":[],"createdAt":"2026-03-07 08:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9056857/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9056857/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565063,"identity":"cf4c3af7-35bd-496f-baf0-9803b898286c","added_by":"auto","created_at":"2026-03-27 12:51:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6562657,"visible":true,"origin":"","legend":"","description":"","filename":"RoSITaMLJ.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9056857/v1_covered_8db70f34-a526-4c65-b5f1-528e079c320c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing Rotation and Scale-Invariant Trajectory Representations for Maritime and Aviation Mobility Data Analysis","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":"Spatio-tempral data, Knowledge representation and reasoning, Human-centered computing","lastPublishedDoi":"10.21203/rs.3.rs-9056857/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9056857/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Spatiotemporal data from connected vehicles, sensors, and GPS-enabled devices increasingly support applications ranging from traffic management to environmental analysis. 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