Decentralized Multi-Agent Reinforcement Learning for Adaptive Traffic Congestion Control: Integrating Deep Q-Networks with Kalman Filter-Based Prediction

Decentralized Multi-Agent Reinforcement Learning for Adaptive Traffic Congestion Control: Integrating Deep Q-Networks with Kalman Filter-Based 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 Decentralized Multi-Agent Reinforcement Learning for Adaptive Traffic Congestion Control: Integrating Deep Q-Networks with Kalman Filter-Based Prediction Younus Hasan Taher, Jit Singh Mandeep, Heba G. Mohamed, Mohamad A. Alawad, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6518175/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 Urban traffic congestion poses a significant challenge to modern cities, impacting mobility, air quality, and overall quality of life. Traditional traffic signal control systems, often based on fixed schedules or simple heuristics, struggle to adapt to dynamic traffic patterns, leading to inefficient traffic flow. This study addresses the pressing need for adaptive and efficient traffic signal control systems capable of responding to real-time traffic conditions across multiple intersections. We propose a novel multi-agent reinforcement learning approach for traffic congestion control, utilizing Deep Q-Network (DQN) algorithms integrated with Kalman filter-based traffic prediction. Our method incorporates a decentralized control architecture, a comprehensive state representation including vehicle counts and waiting times, and an expanded action space covering various traffic signal configurations. A key innovation is our reward model, which balances both congestion reduction and fairness in traffic flow across different directions. Physical sciences/Engineering/Civil engineering Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Applied mathematics 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-6518175","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466253649,"identity":"fee939d3-369a-4cd5-910a-468bd1bf6c49","order_by":0,"name":"Younus Hasan Taher","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACewSTx/zHByDFx8DAjFeLYQNCi8HBGQwMEmyEtBgcQNJymIcoLbcPsEl8bLuTOL8BqMWm4nAdG3vzYQOGGptonFrOJbBJzmx7lrjhAP+HwzlnDkuw8RxLTmA4lpbbgEvLGQY2ad62w4kb5N8YHM5tA2qRyDE+wNhwmLAWsMMsSdLScACohRGqJQGfFsMexmbLGecOG28AajnYcyZdsg3oF4MEPH6x52E+eOND2WFZkMMO/Kiw5ucHhpjEhxobnFoYGBhbJDAFE3AqBwPmD/jlR8EoGAWjYMQDADoMWJYJQcu0AAAAAElFTkSuQmCC","orcid":"","institution":"National University of Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Younus","middleName":"Hasan","lastName":"Taher","suffix":""},{"id":466253650,"identity":"eae92d8e-2a01-40a3-8650-5cf4b657c408","order_by":1,"name":"Jit Singh Mandeep","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Jit","middleName":"Singh","lastName":"Mandeep","suffix":""},{"id":466253651,"identity":"ab18544e-4aee-4aa8-a909-17f1b565e2dc","order_by":2,"name":"Heba G. Mohamed","email":"","orcid":"","institution":"Princess Nourah bint Abdulrahman University","correspondingAuthor":false,"prefix":"","firstName":"Heba","middleName":"G.","lastName":"Mohamed","suffix":""},{"id":466253652,"identity":"c1a2ade7-abdc-498d-9453-e44faaee70c9","order_by":3,"name":"Mohamad A. Alawad","email":"","orcid":"","institution":"Imam Mohammad ibn Saud Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"A.","lastName":"Alawad","suffix":""},{"id":466253653,"identity":"aff612a6-7f63-4c0e-8d73-f2954105a9ad","order_by":4,"name":"MD.SHABIUL ISLAM","email":"","orcid":"","institution":"Multimedia University","correspondingAuthor":false,"prefix":"","firstName":"MD.SHABIUL","middleName":"","lastName":"ISLAM","suffix":""},{"id":466253654,"identity":"02e185d8-e28f-42d4-9f10-6a4dfdcfadb2","order_by":5,"name":"Mohammad Tariqul ISLAM","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Tariqul","lastName":"ISLAM","suffix":""}],"badges":[],"createdAt":"2025-04-24 07:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6518175/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6518175/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89488259,"identity":"9c9ddf70-a689-45ed-86a0-65c526e0144c","added_by":"auto","created_at":"2025-08-20 13:17:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":972408,"visible":true,"origin":"","legend":"","description":"","filename":"Yonessarticle24.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6518175/v1_covered_d30558c5-a649-48d4-b015-15e81acb06bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decentralized Multi-Agent Reinforcement Learning for Adaptive Traffic Congestion Control: Integrating Deep Q-Networks with Kalman Filter-Based 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-6518175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6518175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":" Urban traffic congestion poses a significant challenge to modern cities, impacting mobility, air quality, and overall quality of life. Traditional traffic signal control systems, often based on fixed schedules or simple heuristics, struggle to adapt to dynamic traffic patterns, leading to inefficient traffic flow. This study addresses the pressing need for adaptive and efficient traffic signal control systems capable of responding to real-time traffic conditions across multiple intersections. We propose a novel multi-agent reinforcement learning approach for traffic congestion control, utilizing Deep Q-Network (DQN) algorithms integrated with Kalman filter-based traffic prediction. Our method incorporates a decentralized control architecture, a comprehensive state representation including vehicle counts and waiting times, and an expanded action space covering various traffic signal configurations. A key innovation is our reward model, which balances both congestion reduction and fairness in traffic flow across different directions.","manuscriptTitle":"Decentralized Multi-Agent Reinforcement Learning for Adaptive Traffic Congestion Control: Integrating Deep Q-Networks with Kalman Filter-Based Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 09:54:56","doi":"10.21203/rs.3.rs-6518175/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":"afebd40e-0e27-40d9-9529-92447c711922","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49498265,"name":"Physical sciences/Engineering/Civil engineering"},{"id":49498266,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":49498267,"name":"Physical sciences/Engineering"},{"id":49498268,"name":"Physical sciences/Mathematics and computing"},{"id":49498269,"name":"Physical sciences/Mathematics and computing/Applied mathematics"},{"id":49498270,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2025-08-20T13:09:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-05 09:54:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6518175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6518175","identity":"rs-6518175","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}