Day – ahead traffic flow forecast using LSTM and Cuckoo Search Optimization

Day – ahead traffic flow forecast using LSTM and Cuckoo Search Optimization | 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 Day – ahead traffic flow forecast using LSTM and Cuckoo Search Optimization Rajalakshmi V, Sharon Femi P, Kala A This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4234800/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 To monitor and regulate the traffic flow there is always a need to implement a dynamic and proactive traffic control system. Traffic flow forecasting is an essential and critical requirement for developing a traffic control system in intelligent transportation systems (ITS). In this paper, traffic flow forecasting is done on the England highways dataset of MIDAS site. LSTM networks play a critical role in sequence learning. It is used to forecast the patterns of forecast in the near future. Bio-inspired algorithms are commonly employed in numerous research to optimize the model's parameters. In this work, optimizing the performance of LSTM parameters is initiated with the Cuckoo Search Evolutionary Algorithm. Optimizing weights of various layers improves the performance of LSTM model. The cuckoo search optimization is used in finding the optimum weights and bias values for the LSTM network. The prior traffic flow data is imparted with intelligence using the LSTM – Cuckoo Search (CS) to provide adequate decisions to drivers. The results propagate that the Cuckoo Search has improved the performance of LSTM with the R2 of 0.98. Long Short Term Memory (LSTM) Evolutionary Computing Cuckoo Search Optimization Time series forecasting Parameter tuning 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-4234800","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289918171,"identity":"6225d508-3161-4149-ab60-32aecaa9c840","order_by":0,"name":"Rajalakshmi V","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYJACg4QKOFuCWC1nSNXCwNhGgpsYDG4fflDwcF5dYn97A+OHHwwWeYS1nEszMEjcdjhxxpkDzJI9DBLFBLUAVYG0HDA2kEhgkAb6JbGBsBb2DwaJc+qMDeQfMP8mSgs/Dw/QlgZmOQMJBjbibAFqKTBIOHZYTuJMYptljwERWth42LcZ/qip4+FvP3z4xo+KOsJaQLoMIDQjULEBEeqBgPkBcepGwSgYBaNgxAIAtCgze63K+AIAAAAASUVORK5CYII=","orcid":"","institution":"Sri Venkateswara College of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Rajalakshmi","middleName":"","lastName":"V","suffix":""},{"id":289918172,"identity":"66761767-567f-4fd2-8015-cc2525b86ac4","order_by":1,"name":"Sharon Femi P","email":"","orcid":"","institution":"Sri Venkateswara College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Sharon","middleName":"Femi","lastName":"P","suffix":""},{"id":289918173,"identity":"2e9fdb97-7845-46f3-bb34-cc355aa7e164","order_by":2,"name":"Kala A","email":"","orcid":"","institution":"Sri Venkateswara College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Kala","middleName":"","lastName":"A","suffix":""}],"badges":[],"createdAt":"2024-04-08 07:48:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4234800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4234800/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55942964,"identity":"7c80ff5e-bf86-4032-b1b8-5628c9dc1c29","added_by":"auto","created_at":"2024-05-06 15:49:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":508112,"visible":true,"origin":"","legend":"","description":"","filename":"CuckooSearch.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4234800/v1_covered_2e07f66d-01b2-40c6-9e5e-e78b1d55f3e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Day – ahead traffic flow forecast using LSTM and Cuckoo Search Optimization","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":"Long Short Term Memory (LSTM), Evolutionary Computing, Cuckoo Search Optimization, Time series forecasting, Parameter tuning","lastPublishedDoi":"10.21203/rs.3.rs-4234800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4234800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo monitor and regulate the traffic flow there is always a need to implement a dynamic and proactive traffic control system. Traffic flow forecasting is an essential and critical requirement for developing a traffic control system in intelligent transportation systems (ITS). In this paper, traffic flow forecasting is done on the England highways dataset of MIDAS site. LSTM networks play a critical role in sequence learning. \u0026nbsp;It is used to forecast the patterns of forecast in the near future. Bio-inspired algorithms are commonly employed in numerous research to optimize the model's parameters. In this work, optimizing the performance of LSTM parameters is initiated with the Cuckoo Search Evolutionary Algorithm. Optimizing weights of various layers improves the performance of LSTM model. The cuckoo search optimization is used in finding the optimum weights and bias values for the LSTM network. The prior traffic flow data is imparted with intelligence using the LSTM – Cuckoo Search (CS) to provide adequate decisions to drivers. The results propagate that the Cuckoo Search has improved the performance of LSTM with the R2 of 0.98.\u003c/p\u003e","manuscriptTitle":"Day – ahead traffic flow forecast using LSTM and Cuckoo Search Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-12 13:53:00","doi":"10.21203/rs.3.rs-4234800/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":"d9915b6c-8cac-470b-a67b-b1743fa7bf49","owner":[],"postedDate":"April 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-06T15:47:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-12 13:53:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4234800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4234800","identity":"rs-4234800","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}