Enhancing Urban Transportation Resilience Using Graph Neural Networks with Social Event Integration

Enhancing Urban Transportation Resilience Using Graph Neural Networks with Social Event Integration | 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 Enhancing Urban Transportation Resilience Using Graph Neural Networks with Social Event Integration AKHIL SAI SAMINENI, Joel Kodamanchili, Venkataramana Bandaru This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8373335/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 Urban Traffic Resilience Through Graph Neural Networks and Social Event Integration. Urban traffic resilience plays a vital role in daily transportation systems. Cities face increasing challenges from disruptions such as traffic congestion caused by construction, accidents, and major public events. To enable effective urban mobility planning, it is essential to forecast these disruptions and assess how quickly transportation systems can recover. A novel approach to predicting urban traffic resilience is introduced using Graph Neural Networks (GNNs) combined with integrated social event data. Urban traffic net- works are modeled as dynamic graphs, which serve as simplified representations of traffic infrastructure. In these graphs, nodes represent critical locations such as intersections and roads, while edges indicate connections, including road links and traffic flow. Real-time data from sources such as mainstream media, social media platforms, and event planning systems is incorporated. This allows for consideration of disruptions caused by public events including concerts, protests, and sports gatherings, all of which have significant effects on traffic conditions. This method enhances the prediction of traffic congestion, recovery durations, and system adaptability. An advanced tool for urban traffic man- agement is thereby provided. The model has been evaluated using real-world datasets, demonstrating its effectiveness in forecasting disruptions and offering strategies for improving traffic flow and resilience. Urban Transportation Resilience Graph Neural Networks (GNNs) Traffic Prediction Social Event Data Smart Cities Spatio-Temporal Analysis Machine Learning Real-Time Traffic Management 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-8373335","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585181794,"identity":"346d8215-cd86-4931-a3f4-5fca86544155","order_by":0,"name":"AKHIL SAI SAMINENI","email":"","orcid":"","institution":"Jawaharlal Nehru Technological University, Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"AKHIL","middleName":"SAI","lastName":"SAMINENI","suffix":""},{"id":585181795,"identity":"da1177a6-2bf1-45b0-9f2d-d4b1c94eb02e","order_by":1,"name":"Joel Kodamanchili","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYBACxgYGxoMNDMxgDjODgQUPP4iVUIBXCwOyFgkeyQaQFgP8NiFpYZBgMDgAYuLRwtzefODgjApred32swc/FxRIyBifX5344YEBgzy/2AHsDus5lnBww5l0w21n8pKlZwAdZnbj7WYJoMMMZ85OwK5lRo7BwYdthxm3HcgxY+YBazm7AaQlweA2Li35H0Ba7LedfwPRYjzj7OYf+LXkMBzc2HY4cdsNqC0G/L3b8NvSc8zg4Iwz6cnbbrwxlgZpkbjBu80iwUACp18M25sfPuypsLbddj7H8DPPHxt7/v6zm2/+qLCR55fGoaUBQ0gCrFICq3IQkMcU4j+AU/UoGAWjYBSMTAAALGBjvAnSgrkAAAAASUVORK5CYII=","orcid":"","institution":"Jawaharlal Nehru Technological University, Hyderabad","correspondingAuthor":true,"prefix":"","firstName":"Joel","middleName":"","lastName":"Kodamanchili","suffix":""},{"id":585181796,"identity":"d96ecb07-d0c5-43aa-9bf9-e6a05c2868dd","order_by":2,"name":"Venkataramana Bandaru","email":"","orcid":"","institution":"Jawaharlal Nehru Technological University, Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Venkataramana","middleName":"","lastName":"Bandaru","suffix":""}],"badges":[],"createdAt":"2025-12-16 07:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8373335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8373335/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106290631,"identity":"bc977ac8-df6a-4a6f-9cfb-0aa2ba3bd534","added_by":"auto","created_at":"2026-04-07 07:44:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":485713,"visible":true,"origin":"","legend":"","description":"","filename":"GNN1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8373335/v1_covered_e8a7bceb-e79e-404d-8086-dac3effb854d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEnhancing Urban Transportation Resilience Using Graph Neural Networks with Social Event Integration\u003c/p\u003e","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":"Urban Transportation Resilience, Graph Neural Networks (GNNs), Traffic Prediction, Social Event Data, Smart Cities, Spatio-Temporal Analysis, Machine Learning, Real-Time Traffic Management","lastPublishedDoi":"10.21203/rs.3.rs-8373335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8373335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban Traffic Resilience Through Graph Neural Networks and Social Event Integration. Urban traffic resilience plays a vital role in daily transportation systems. Cities face increasing challenges from disruptions such as traffic congestion caused by construction, accidents, and major public events. To enable effective urban mobility planning, it is essential to forecast these disruptions and assess how quickly transportation systems can recover. A novel approach to predicting urban traffic resilience is introduced using Graph Neural Networks (GNNs) combined with integrated social event data. Urban traffic net- works are modeled as dynamic graphs, which serve as simplified representations of traffic infrastructure. In these graphs, nodes represent critical locations such as intersections and roads, while edges indicate connections, including road links and traffic flow. Real-time data from sources such as mainstream media, social media platforms, and event planning systems is incorporated. This allows for consideration of disruptions caused by public events including concerts, protests, and sports gatherings, all of which have significant effects on traffic conditions. This method enhances the prediction of traffic congestion, recovery durations, and system adaptability. An advanced tool for urban traffic man- agement is thereby provided. 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