Forecasting traffic flow time series with Vine-Transform ARMA Copula models

Forecasting traffic flow time series with Vine-Transform ARMA Copula models | 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 Forecasting traffic flow time series with Vine-Transform ARMA Copula models Sara Selvaggia Guerini, Rodolfo Metulini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7011611/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Quality & Quantity → Version 1 posted You are reading this latest preprint version Abstract The prediction of traffic flows in urban areas is gaining importance in order to carry out urban planning for early warning systems and optimized logistics. Hence, there is a growing need for simple and high-performing statistical models. This study leverages the vine-transform autoregressive moving-average copula model to predict traffic data, with particular emphasis on evaluating forecasting performance. To do so, real-life data on origin-destination signals extracted from mobile phone data have been used. Performance evaluation was conducted using the rank-graduation box approach along with a moving window cross-validation strategy, incorporating rank-graduation accuracy for precision and rank-graduation explainability for component analysis. As a benchmark for comparison, the VARX-DHR model was used. Preliminary results reveal that the vine-transform ARMA copula approach performs well in terms of accuracy. Furthermore, we show that the copula component presents greater explainability compared to the autoregressive and moving average components. Residual diagnostics show significantly lower autocorrelation and partial autocorrelation with respect to the original data, and that they are approximately normally distributed. The developed method could provide valuable insights supporting urban planners and analysts in making informed decisions. Vine-Transform ARMA Copulas Rank-Graduation Approach Predictive Performance Time Series Modeling Urban Traffic Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Quality & Quantity → 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. 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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-7011611","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524535963,"identity":"bbcad6a6-aabc-4719-8b39-cc754f05659c","order_by":0,"name":"Sara Selvaggia Guerini","email":"data:image/png;base64,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","orcid":"","institution":"University of Bergamo","correspondingAuthor":true,"prefix":"","firstName":"Sara","middleName":"Selvaggia","lastName":"Guerini","suffix":""},{"id":524535964,"identity":"d5854153-cc55-42fa-a835-5e1cd8a1003e","order_by":1,"name":"Rodolfo Metulini","email":"","orcid":"","institution":"University of Bergamo","correspondingAuthor":false,"prefix":"","firstName":"Rodolfo","middleName":"","lastName":"Metulini","suffix":""}],"badges":[],"createdAt":"2025-06-30 14:23:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7011611/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7011611/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11135-025-02558-0","type":"published","date":"2026-02-02T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93688995,"identity":"41a07a75-1f91-4d99-83d2-010a4b005e93","added_by":"auto","created_at":"2025-10-16 13:47:33","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4712,"visible":true,"origin":"","legend":"","description":"","filename":"4d72d0f2cb2a4b6ea547e7e2e7db6e04.json","url":"https://assets-eu.researchsquare.com/files/rs-7011611/v1/ba536844d515f127266dc746.json"},{"id":102235038,"identity":"0510332f-23a5-4141-bf00-df1376587a01","added_by":"auto","created_at":"2026-02-09 16:14:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5727659,"visible":true,"origin":"","legend":"","description":"","filename":"ForecastingTrafficFlowTimeSerieswithVineTransformARMACopulaModels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7011611/v1_covered_9bf6d678-69e7-46ba-b241-41943b2a9065.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecasting traffic flow time series with Vine-Transform ARMA Copula models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Vine-Transform ARMA Copulas, Rank-Graduation Approach, Predictive Performance, Time Series Modeling, Urban Traffic Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7011611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7011611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The prediction of traffic flows in urban areas is gaining importance in order to carry out urban planning for early warning systems and optimized logistics. 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