Research on identification method for spatial master data of railroad system

Research on identification method for spatial master data of railroad system | 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 Research on identification method for spatial master data of railroad system Maolin Zhang, Tao Shen, Liang Huo, Yaodong Yang, Shiqi Zhouwen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4453233/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 Railway spatial master data is crucial asset data within the railway system, accurately reflecting the geographic locations and spatial information of core entities within the system, enabling railway companies to make a series of spatial decisions. In view of this, this article proposes a railway spatial master data recognition method based on the rough set-cloud model to identify spatial master data from massive railway data. This method first selects indicators for identification based on the characteristics of spatial data and master data. Then, it determines the weights of the identification indicators based on rough set theory. Next, it uses a cloud model to transform the indicators from qualitative to quantitative, calculate the membership degrees of the indicators, and generate an indicator cloud reflecting the characteristics of the indicators. Finally, by combining membership degrees with indicator weights and prioritizing maximum membership degrees, accurate identification of massive railway data is carried out. This method, compared to traditional recognition approaches, eliminates the subjective bias that expert scoring may introduce. It also increases the number of recognition indicators, enhancing accuracy and specificity in identification. The results indicate that the calculation method is consistent with the expected results, demonstrating the effectiveness and reliability of the method in the field of railway spatial master data identification. Physical sciences/Engineering/Civil engineering Physical sciences/Mathematics and computing/Computational science Spatial master data identification rough sets cloud model forward cloud generator 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-4453233","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":311146302,"identity":"c0e40ecf-bc5c-4921-9cac-a8e5099ec979","order_by":0,"name":"Maolin Zhang","email":"","orcid":"","institution":"Beijing University of Civil Engineering and Architecture","correspondingAuthor":false,"prefix":"","firstName":"Maolin","middleName":"","lastName":"Zhang","suffix":""},{"id":311146303,"identity":"84a77102-29e7-4cce-91fe-509abd4ecc09","order_by":1,"name":"Tao Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACCQglZwDlMzYQpeUAA4Mx6VoSNxCtRX5287HHH2ps0rdLJD988IPBRnbDAeZnD/BpMbhzLN3gwLG03J0z0owNexjSjDccYDM3wKtFIsdM4gDb4dwNN3LYJHgYDiduOMDDJoHXYTPyv0kc+Hc43eBGDvvPPwz/CWthABl+sO1wAlALGzMPwwHCWgxupJlJnO1LM9zZ88xYWsYg2XjmYTYzAg5LfiZR8c1G3pw9+eHHNxV2sn3Hm5/hdxiapUDMTIL6UTAKRsEoGAXYAQA1e0qeCxzoNgAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Civil Engineering and Architecture","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Shen","suffix":""},{"id":311146305,"identity":"3ff044a5-1458-4dae-aea8-47105b18526f","order_by":2,"name":"Liang Huo","email":"","orcid":"","institution":"Beijing University of Civil Engineering and Architecture","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Huo","suffix":""},{"id":311146306,"identity":"24b1d2ee-9806-43d7-afd1-ff83ba819904","order_by":3,"name":"Yaodong Yang","email":"","orcid":"","institution":"State Geospatial Information Center","correspondingAuthor":false,"prefix":"","firstName":"Yaodong","middleName":"","lastName":"Yang","suffix":""},{"id":311146307,"identity":"19e3eefb-b961-49bb-a82f-2611cbd69094","order_by":4,"name":"Shiqi Zhouwen","email":"","orcid":"","institution":"Beijing University of Civil Engineering and Architecture","correspondingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Zhouwen","suffix":""}],"badges":[],"createdAt":"2024-05-21 08:20:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4453233/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4453233/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64790404,"identity":"0d3604c8-de14-4877-b521-4737147d948f","added_by":"auto","created_at":"2024-09-18 22:34:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":584163,"visible":true,"origin":"","legend":"","description":"","filename":"Researchonidentificationmethodforspatialmasterdataofrailroadsystem.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4453233/v1_covered_a6425241-70a9-4ed5-a5f2-d2ac9731ceaa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on identification method for spatial master data of railroad system","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":"Spatial master data identification, rough sets, cloud model, forward cloud generator","lastPublishedDoi":"10.21203/rs.3.rs-4453233/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4453233/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Railway spatial master data is crucial asset data within the railway system, accurately reflecting the geographic locations and spatial information of core entities within the system, enabling railway companies to make a series of spatial decisions. In view of this, this article proposes a railway spatial master data recognition method based on the rough set-cloud model to identify spatial master data from massive railway data. This method first selects indicators for identification based on the characteristics of spatial data and master data. Then, it determines the weights of the identification indicators based on rough set theory. Next, it uses a cloud model to transform the indicators from qualitative to quantitative, calculate the membership degrees of the indicators, and generate an indicator cloud reflecting the characteristics of the indicators. Finally, by combining membership degrees with indicator weights and prioritizing maximum membership degrees, accurate identification of massive railway data is carried out. This method, compared to traditional recognition approaches, eliminates the subjective bias that expert scoring may introduce. It also increases the number of recognition indicators, enhancing accuracy and specificity in identification. The results indicate that the calculation method is consistent with the expected results, demonstrating the effectiveness and reliability of the method in the field of railway spatial master data identification.","manuscriptTitle":"Research on identification method for spatial master data of railroad system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 05:07:08","doi":"10.21203/rs.3.rs-4453233/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"d0ecfd34-b336-4ff0-8fb6-29640664ff51","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32883337,"name":"Physical sciences/Engineering/Civil engineering"},{"id":32883338,"name":"Physical sciences/Mathematics and computing/Computational science"}],"tags":[],"updatedAt":"2024-09-18T22:26:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 05:07:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4453233","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4453233","identity":"rs-4453233","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}