An optimized Q algorithm based on Slot prediction

An optimized Q algorithm based on Slot prediction | 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 An optimized Q algorithm based on Slot prediction Jiahao Wen, Jiacheng Luo, Jian Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4361967/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 High throughput is crucial for Electronic Toll Collection(ETC) systems to effectively reduce traffic congestion. However, traditional algorithms face challenges such as slot waste and slow adjustments in frame length. To tackle these issues, a Slot Prediction Q algorithm(SPQ) has been introduced, which combines the Vogt-Ⅱ Prediction algorithm and slot grouping concept to improve the initial Q-value by predicting the first frame. This algorithm can quickly predict the number of tags to be identified based on slot utilization, speeding up the Q-value adjustment process when slot utilization is low. Additionally, the Markov decision chain is used to determine the optimal relationship between the number of slot groupings x and Q-value. The Whale Optimization Algorithm(WOA) is employed to optimize the relationship between the learning rate C and Q-value in the traditional Q algorithm. Simulation results show that SPQ significantly reduces the total number of slots used during the reading process, thereby enhancing the ETC throughput. Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Computer science 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-4361967","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":301578291,"identity":"675c80d6-1210-497d-a198-95b22e257fe1","order_by":0,"name":"Jiahao Wen","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Wen","suffix":""},{"id":301578292,"identity":"063a3024-ef7c-4112-97a2-ab3d3b3420cb","order_by":1,"name":"Jiacheng Luo","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Luo","suffix":""},{"id":301578293,"identity":"40d9d890-47ca-4a21-aba5-1ffabaf24ea2","order_by":2,"name":"Jian Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACxmYkjsEHAxs70rQYzihISybNSmaeD4cYGwiqaudO3czbZpO4nX+NQbGNwQFmBvbDRzfgdxjvttu8bWmJO2e8MTDOMbjDx8CTlnaDCC2HEzfcOAPS8oyZQYLHjAQtFgaHGRuI13K+x8CYgVgtN+ecSzPecIOtwLDHIC2ZjZBfDPvPbrvxpsxGdsP5w9sMfvyxseNnP3wMv5YGBgYmXjYgSyLDzAAkwoZPOQjIgxz34w+Q5D/++AEh1aNgFIyCUTAyAQAMclE6jpTAegAAAABJRU5ErkJggg==","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-05-03 05:08:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4361967/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4361967/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61486490,"identity":"fb74f0fa-d397-4b53-9f9c-0e7a99c1a9ac","added_by":"auto","created_at":"2024-07-31 09:50:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":671636,"visible":true,"origin":"","legend":"","description":"","filename":"AnoptimizedQalgorithmbasedonslotprediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4361967/v1_covered_ee42e556-ab05-4d7e-9dc0-f8a19bdfd0c1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An optimized Q algorithm based on Slot prediction","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4361967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4361967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh throughput is crucial for Electronic Toll Collection(ETC) systems to effectively reduce traffic congestion. However, traditional algorithms face challenges such as slot waste and slow adjustments in frame length. To tackle these issues, a Slot Prediction Q algorithm(SPQ) has been introduced, which combines the Vogt-Ⅱ Prediction algorithm and slot grouping concept to improve the initial Q-value by predicting the first frame. This algorithm can quickly predict the number of tags to be identified based on slot utilization, speeding up the Q-value adjustment process when slot utilization is low. Additionally, the Markov decision chain is used to determine the optimal relationship between the number of slot groupings \u003cem\u003ex\u003c/em\u003e and Q-value. The Whale Optimization Algorithm(WOA) is employed to optimize the relationship between the learning rate \u003cem\u003eC\u003c/em\u003e and Q-value in the traditional Q algorithm. Simulation results show that SPQ significantly reduces the total number of slots used during the reading process, thereby enhancing the ETC throughput.\u003c/p\u003e","manuscriptTitle":"An optimized Q algorithm based on Slot prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-13 18:54:53","doi":"10.21203/rs.3.rs-4361967/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":"da304edc-b9ae-4b53-b42e-a44cb3b3f6f5","owner":[],"postedDate":"May 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31818892,"name":"Physical sciences/Mathematics and computing/Information technology"},{"id":31818893,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2024-07-31T09:42:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-13 18:54:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4361967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4361967","identity":"rs-4361967","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}