Assessing Subway Ridership Resilience under Extreme Weather with Vine Copula Modeling

Assessing Subway Ridership Resilience under Extreme Weather with Vine Copula Modeling | 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 Article Assessing Subway Ridership Resilience under Extreme Weather with Vine Copula Modeling Yan Guo, Brian Yueshuai He, Joseph Y.J. Chow, Zhiya Su, Omar Wani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8041792/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Extreme weather poses increasing challenges to urban transit systems, yet the resilience of subway ridership under such conditions remains insufficiently understood. This study develops an hour-specific vine copula framework for New York City subway ridership modeling, decomposing high-dimensional inter-station relationships into bivariate components while preserving non-linear and asymmetric dependencies. The methodology captures time-varying dependencies, generates realistic ridership distributions under diverse weather conditions, and enables quantitative assessment of ridership resilience to extreme events. Validation demonstrates strong performance, with 83 percent of scenarios achieving Kullback–Leibler divergence below 0.15. Results reveal a dynamic dependence structure across stations that varies under different environmental conditions. Results indicate that Manhattan core stations exhibit higher ridership resilience, whereas outer borough stations are more vulnerable. Heavy precipitation produces the most severe peak-hour impacts, while extreme cold primarily reduces off-peak ridership. For example, heavy rain during peak hours leads to a median of 19.3 percent decline (95 percent interval: −19.6, − 3.4) to Penn Station ridership, whereas extreme heat during off-peak hours reduces Jackson Heights ridership by 14.8 percent (95 percent interval: −31.4, − 12.7). This framework provides a data-driven foundation for assessing ridership resilience and guiding climate adaptation and equitable transit investment in metropolitan systems. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Vine Copula Spatio-temporal Modeling Subway Ridership Data Synthesis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Dec, 2025 Reviews received at journal 19 Dec, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers invited by journal 17 Nov, 2025 Editor assigned by journal 10 Nov, 2025 Submission checks completed at journal 10 Nov, 2025 First submitted to journal 05 Nov, 2025 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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Modeling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"npj-sustainable-mobility-and-transport","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Mobility and Transport](https://www.nature.com/npjsustainmobil)","snPcode":"44333","submissionUrl":"https://submission.springernature.com/new-submission/44333/3","title":"npj Sustainable Mobility and 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This study develops an hour-specific vine copula framework for New York City subway ridership modeling, decomposing high-dimensional inter-station relationships into bivariate components while preserving non-linear and asymmetric dependencies. The methodology captures time-varying dependencies, generates realistic ridership distributions under diverse weather conditions, and enables quantitative assessment of ridership resilience to extreme events. Validation demonstrates strong performance, with 83 percent of scenarios achieving Kullback\u0026ndash;Leibler divergence below 0.15. Results reveal a dynamic dependence structure across stations that varies under different environmental conditions. Results indicate that Manhattan core stations exhibit higher ridership resilience, whereas outer borough stations are more vulnerable. Heavy precipitation produces the most severe peak-hour impacts, while extreme cold primarily reduces off-peak ridership. 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This framework provides a data-driven foundation for assessing ridership resilience and guiding climate adaptation and equitable transit investment in metropolitan systems.\u003c/p\u003e","manuscriptTitle":"Assessing Subway Ridership Resilience under Extreme Weather with Vine Copula Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:43:17","doi":"10.21203/rs.3.rs-8041792/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-23T18:43:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T13:58:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34226834642725475117685403724470527363","date":"2025-11-30T13:40:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T12:18:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22375693430573118110825565680193349833","date":"2025-11-18T15:44:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-18T02:02:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T08:33:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-10T05:34:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Sustainable Mobility and Transport","date":"2025-11-05T21:00:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-mobility-and-transport","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Mobility and Transport](https://www.nature.com/npjsustainmobil)","snPcode":"44333","submissionUrl":"https://submission.springernature.com/new-submission/44333/3","title":"npj Sustainable Mobility and Transport","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1640b84-24a5-4281-b87a-01225324a295","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58200508,"name":"Earth and environmental sciences/Climate sciences"},{"id":58200509,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-03-02T21:39:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:43:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8041792","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8041792","identity":"rs-8041792","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}