A Mobile Data-Enhanced Framework for Spatial-Temporal Analysis of Subway Competitiveness and Equity Implications

A Mobile Data-Enhanced Framework for Spatial-Temporal Analysis of Subway Competitiveness and Equity Implications | 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 A Mobile Data-Enhanced Framework for Spatial-Temporal Analysis of Subway Competitiveness and Equity Implications Caixia Li, Hunan Deng, Jiachao Chen, Junhui Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7257120/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Transportation → Version 1 posted 9 You are reading this latest preprint version Abstract Conventional subway analyses relying on static IC card data or limited surveys fail to capture the complex, dynamic interactions between passenger flows, land use patterns, and multimodal transportation choices in high-density urban environments. This study overcomes these methodological limitations by developing an innovative analytical framework that synergistically integrates large-scale mobile phone positioning data with traditional transport surveys and automated fare collection records. Using Guangzhou's extensive metro system (247 stations across 531 km) as a representative case study, we employ advanced data fusion techniques and machine learning algorithms to reconstruct complete travel chains and dynamically delineate station service areas with unprecedented spatial-temporal resolution. Our hybrid methodology combines multinomial logit modeling with random forest classification to systematically quantify subway competitiveness across different urban contexts, revealing three key findings: (1) distinct spatial thresholds for effective service areas (800m radius in central business districts vs. 2km in suburban corridors), (2) an inverted U-shaped relationship between parking supply-demand ratios and mode share with optimal balance at 1.75, and (3) significant equity disparities where low-income suburban commuters experience 2 times higher spatiotemporal costs than central city residents. The proposed framework provides urban planners with a robust, scalable tool for transit network optimization, offering particular value for rapidly urbanizing megacities in Asia and other developing regions. By effectively bridging cutting-edge big data analytics with established transportation modeling approaches, this research makes dual contributions: advancing theoretical understanding of fractal urban mobility patterns while delivering practical, data-driven strategies for sustainable transit development and equitable accessibility planning in high-density environments. Cell phone data Machine learning algorithms Spatial-Temporal resolution Catchment areas Competitive advantage Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Transportation → Version 1 posted Editorial decision: Revision requested 02 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 12 Aug, 2025 Submission checks completed at journal 05 Aug, 2025 First submitted to journal 30 Jul, 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. 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. 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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-7257120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502157228,"identity":"a1dc9990-8736-48d1-9b5b-a3cf2657179c","order_by":0,"name":"Caixia Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFACxgYGBgMGGTb2xgcgLg8fsVp42HgOG4C1sBFrFw+DRDJYCwNBLfzthxs//CgAukfyMeNj3hw7GTYG5oePbuDRInEmsVmyB+Qw6WRmw5nbkoEOYzM2zsGjxYAhsY2BB6wl/5jEx23MQC1ANl4t/A/bGP+AtEgeZpNI3FZPhBaJxDZmsC0SzGxAWw4T1iJx42GztIyBBDCQwX45zsPGTMAv/P3pDz+++WMjJ99+GBhi26rt+dmbHz7GpwVmGRKbmbDyUTAKRsEoGAUEAAA3fzbpGXb43QAAAABJRU5ErkJggg==","orcid":"","institution":"Guangdong Communication Polytechnic","correspondingAuthor":true,"prefix":"","firstName":"Caixia","middleName":"","lastName":"Li","suffix":""},{"id":502157229,"identity":"eb8d3d24-02d4-4632-939f-244c7d9b7f40","order_by":1,"name":"Hunan Deng","email":"","orcid":"","institution":"Guangdong Communication Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Hunan","middleName":"","lastName":"Deng","suffix":""},{"id":502157230,"identity":"56eef333-8040-4cce-8f79-8bda97f33bab","order_by":2,"name":"Jiachao Chen","email":"","orcid":"","institution":"Guangzhou Transportation Planning and Research Institute Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jiachao","middleName":"","lastName":"Chen","suffix":""},{"id":502157232,"identity":"b13e55e7-5eb9-40eb-9772-82c7248a19e8","order_by":3,"name":"Junhui Li","email":"","orcid":"","institution":"Guangdong Communication Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Junhui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-31 02:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7257120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7257120/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11116-026-10741-x","type":"published","date":"2026-03-13T16:00:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104740291,"identity":"bc86d158-4947-4713-b50d-42575443a325","added_by":"auto","created_at":"2026-03-16 16:16:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1576135,"visible":true,"origin":"","legend":"","description":"","filename":"AMobileDataEnhancedFrameworkforSpatialTemporalAnalysisofSubwayCompetitivenessandEquityImplications.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7257120/v1_covered_86bbc67c-2ab9-4501-8bf8-9538d72573b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Mobile Data-Enhanced Framework for Spatial-Temporal Analysis of Subway Competitiveness and Equity Implications","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":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cell phone data, Machine learning algorithms, Spatial-Temporal resolution, Catchment areas, Competitive advantage","lastPublishedDoi":"10.21203/rs.3.rs-7257120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7257120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConventional subway analyses relying on static IC card data or limited surveys fail to capture the complex, dynamic interactions between passenger flows, land use patterns, and multimodal transportation choices in high-density urban environments. 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