Optimization Path of Tourist Flows in China base on Graph Convolutional Neural Network

Optimization Path of Tourist Flows in China base on Graph Convolutional Neural Network | 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 Optimization Path of Tourist Flows in China base on Graph Convolutional Neural Network Aihua Gu, Chenting Ge, Yaqi Fan, XinYue Zhao, Shuangshuang Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5708344/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 Tourism route planning is a crucial issue that requires optimization. This article uses linear programming and sorting algorithms to screen the "Top Ten Must-Visit" destinations among 352 cities, enhancing travel efficiency and representation. Innovatively, it integrates integer programming, ant colony algorithm, simulated annealing algorithm, particle swarm optimization algorithm, genetic algorithm, and graph convolutional neural network (GCN) for path planning. GCN constructs tourism maps with cities as nodes and transportation as edges, optimizing parameters through hierarchical convolution and feature aggregation. Each model is trained for 400 iterations, effectively extracting spatial features from the topological maps. Compared with several other algorithms, GCN has stronger fitting learning ability and the obtained embedding vectors are of higher quality. In evaluating tourist aspirations, designing optimal routes, cost optimization, and personalized route creation, hidden city relationships are explored. The optimal route costs 2418 yuan and lasts 90 hours, and maximizes visitation while ensuring safety. Cost optimization reduces the total cost to 1590 yuan for 11 cities. The personalized mountain route within 144 hours, costing 1005 yuan, showcases customized services. These innovations maximize urban exploration and travel experience quality, providing technical support and strategic guidance for richer and more profound travel. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Statistics Particle Swarm Algorithm Simulated Annealing Algorithm Genetic Algorithms Graph Convolutional Neural Network 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-5708344","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":436478608,"identity":"24944bbc-9ce5-47a3-b6bf-32e1830f4e08","order_by":0,"name":"Aihua Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJCCw0DMwy//+OCDhIoa4rXISDakJRs8OHOMOC3MQGxjcCDHTPJhCzNh5fIRyQ8PF7bZ8RgcOJZWkdjAxsDf3p2AV4vhjTSDwzPbknkkDzYfu5G4Q4ZB4szZDfi1zEgwOMzbxszDd5gt7UbiGTYGA4lcQlrSPwC11PMwHOMxK0hsYyasRV4iB2TLYR6BMzxmDERpMeB5U3CY59xxHskZbMkSCWeO8RD0i3x7+ubPPGXV9vwSzAc//qiokeNv7yVgywE0AR68ysG2NBBUMgpGwSgYBSMeAADjEkq7eJVE0gAAAABJRU5ErkJggg==","orcid":"","institution":"Yancheng Teachers University","correspondingAuthor":true,"prefix":"","firstName":"Aihua","middleName":"","lastName":"Gu","suffix":""},{"id":436478609,"identity":"d3d50f27-2187-426c-b410-db7e2019351d","order_by":1,"name":"Chenting Ge","email":"","orcid":"","institution":"Yancheng Teachers University","correspondingAuthor":false,"prefix":"","firstName":"Chenting","middleName":"","lastName":"Ge","suffix":""},{"id":436478610,"identity":"a6b999e9-2eb7-4267-983c-011a8de872f6","order_by":2,"name":"Yaqi Fan","email":"","orcid":"","institution":"Yancheng Teachers University","correspondingAuthor":false,"prefix":"","firstName":"Yaqi","middleName":"","lastName":"Fan","suffix":""},{"id":436478611,"identity":"d72cce30-463d-4dd6-b4fb-f2a2240eb17a","order_by":3,"name":"XinYue Zhao","email":"","orcid":"","institution":"Nanjing Tech University","correspondingAuthor":false,"prefix":"","firstName":"XinYue","middleName":"","lastName":"Zhao","suffix":""},{"id":436478612,"identity":"e480731a-86df-4848-915d-cca5f4087f94","order_by":4,"name":"Shuangshuang Chen","email":"","orcid":"","institution":"Yancheng Teachers University","correspondingAuthor":false,"prefix":"","firstName":"Shuangshuang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-12-25 00:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5708344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5708344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80576191,"identity":"421b5853-7417-4fb3-90a3-9fc79a057f50","added_by":"auto","created_at":"2025-04-14 22:01:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1272019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5708344/v1_covered_2c7c7ba9-2d71-495f-8584-7deb8e15b164.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization Path of Tourist Flows in China base on Graph Convolutional Neural Network","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":"Particle Swarm Algorithm, Simulated Annealing Algorithm, Genetic Algorithms, Graph Convolutional Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-5708344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5708344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTourism route planning is a crucial issue that requires optimization. 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