Evolutionary game research on the decision-making of shared bike placement quantity based on dynamic and static punishment mechanisms

Evolutionary game research on the decision-making of shared bike placement quantity based on dynamic and static punishment mechanisms | 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 Evolutionary game research on the decision-making of shared bike placement quantity based on dynamic and static punishment mechanisms Luyao Jiang, Xiaoping Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3960954/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 This paper optimizes the total amount of shared bike placement from the supply side. Firstly, we used the evolutionary game method to study the dynamic evolution process of the decision-making of government departments and bike-sharing enterprises about the amount of placement. Secondly, we analyze the stability of the equilibrium point in the game system. Finally, we use MATLAB simulation to analyze the stability of its evolution, and then discuss the influence of the core parameters on the evolution of the behavior of the participating parties. The results show that solving the problem of the massive placement of shared bikes requires the government to participate and play a leading role. When the benefit of strict government regulation is less than the cost, a dynamic punishment mechanism should be used. When the benefit is greater than the cost, a static punishment mechanism should be used. Under the static punishment mechanism, the government’s strategy is insensitive to changes in the amount of punishment. But under the dynamic punishment mechanism, the amount of punishment is negatively correlated with the probability of strict government regulation. So the government can reduce its regulatory costs by increasing the amount of punishment. Shared bikes Optimal placement Static penalization Dynamic penalization Evolutionary game MATLAB simulation 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. 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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-3960954","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273333814,"identity":"87c9327e-ad21-4afa-ba20-01b850fc2385","order_by":0,"name":"Luyao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACfvbmg49/VEjIARkHiNMi2XMs2ZjhjI0xkJFAnBaDGzlmwoxtaYkbbvgYEOmyAzlmzIVth40NbvB8vPGGwU5Ot4GADsaGY2WPZ5w7LCd5u3ez5RyGZGOzAwS0MDM2bzfgKTtszHfn7DZpHoYDidsIaWFjZjCT4GE7nNhwI+cZcVp42FjMpHmA3p9wI4eNOC1AG5INZ0AC2dhyjgERfrG///jggw+QqHx4402FnRxBLWhWEhs1SFpI1TEKRsEoGAUjAgAAfXVH0bE9F/oAAAAASUVORK5CYII=","orcid":"","institution":"Xi’an University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Luyao","middleName":"","lastName":"Jiang","suffix":""},{"id":273333815,"identity":"a9500243-b141-403f-ad5c-3850aeac3d09","order_by":1,"name":"Xiaoping Wu","email":"","orcid":"","institution":"Xi’an University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-02-16 10:33:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3960954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3960954/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53521121,"identity":"e510291c-7bd1-4421-a132-5c2cfbf461ee","added_by":"auto","created_at":"2024-03-27 03:35:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":835428,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3960954/v1_covered_a494a7bb-7528-43fc-92e7-e478b1cebe32.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evolutionary game research on the decision-making of shared bike placement quantity based on dynamic and static punishment mechanisms","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":"Shared bikes, Optimal placement, Static penalization, Dynamic penalization, Evolutionary game, MATLAB simulation","lastPublishedDoi":"10.21203/rs.3.rs-3960954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3960954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper optimizes the total amount of shared bike placement from the supply side. 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