Exploring the Potential of Crowdsourced Traffic Data for Improved Traffic Predictions: A Big Data Approach

Exploring the Potential of Crowdsourced Traffic Data for Improved Traffic Predictions: A Big Data Approach | 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 Exploring the Potential of Crowdsourced Traffic Data for Improved Traffic Predictions: A Big Data Approach Farid Azizi, Wei Zhang, Ayesha Malik, Zhou Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3914435/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 This paper presents a groundbreaking approach to traffic prediction through the integration of crowdsourced data within a big data analytics framework. The proposed methodology addresses the limitations of traditional traffic prediction models and machine learning approaches by leveraging the collective intelligence from diverse sources, including navigation apps, social media, and connected vehicles. A dynamic algorithm continuously updates the model parameters in real-time, adapting to evolving traffic patterns in urban environments. Comprehensive experiments, conducted on the UrbanTrafficFlow (UTF) and CityMobilityPatterns (CMP) datasets, demonstrate the superior predictive accuracy and adaptability of the proposed model compared to existing methods, including LSTM-based models, spatiotemporal graph convolutional networks (ST-GCN), and traditional statistical models. The integration of diverse data sources showcases the model's ability to enhance accuracy, and the impact of crowdsourced data integration is highlighted. This research contributes to the advancement of intelligent transportation systems and big data analytics, paving the way for more resilient and efficient urban mobility solutions in the era of smart cities. 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-3914435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270290663,"identity":"c688d547-f47c-4b46-b566-b65151e5135a","order_by":0,"name":"Farid Azizi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Farid","middleName":"","lastName":"Azizi","suffix":""},{"id":270290664,"identity":"d0afa150-ecd0-43d2-bce8-8a1ed90073d1","order_by":1,"name":"Wei Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":270290665,"identity":"21698df0-0094-4c91-8d29-a5d71d35caa8","order_by":2,"name":"Ayesha Malik","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Malik","suffix":""},{"id":270290666,"identity":"d32747b8-6ffe-4a24-9002-6f8720108b40","order_by":3,"name":"Zhou Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYDACCSBmbEASMGAH8QwsSNHCcwBESpCiRSIBJo4dyM9uPib5c4dNHoP04cMfPtTckzeXfH51w48CCQb+9u4EbFoY5xxLk+Y9k1bMwJeWJjnjWLHhztk5ZTd7gA6TOHN2AzYtzBI5ZtKMbYcTG3h4zJh52BISDG7npN3gAWoxkMjFqoUNqEXyZ9t/oBb+z5///ANquXkm7eYfPFp4gFokeNsOgGxhAFoH1HKD/dhtfLZISKQlW/O2JSe28bCZSfb2JRhuOJPDdlvGQIIHl1/kZyQfvPmzzS6xn4f58Ycf3xLkDY4ff3bzzR8bOf72XqxaEJ5CcqwBmMSrHA2wPyBF9SgYBaNgFAx/AACORF5UeUjw1AAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2024-01-31 16:12:20","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-3914435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3914435/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50472331,"identity":"93a63743-77f0-4857-9cd6-24bc39b217bb","added_by":"auto","created_at":"2024-02-01 04:43:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":213539,"visible":true,"origin":"","legend":"","description":"","filename":"main.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3914435/v1_covered_54955ca2-32fa-41a7-94f4-cbed1d728ffb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eExploring the Potential of Crowdsourced Traffic Data for Improved Traffic Predictions: A Big Data Approach\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-3914435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3914435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a groundbreaking approach to traffic prediction through the integration of crowdsourced data within a big data analytics framework. 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