{"paper_id":"103fcce8-9cbc-4fdc-91f2-1d2bc776673d","body_text":"Coordination of Urban Traffic Lights based on the Analysis of Mean Travel Speeds by Artificial Neural Networks and Deep Reinforcement Learning: A Case Study in the City of Yaoundé | 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 Method Article Coordination of Urban Traffic Lights based on the Analysis of Mean Travel Speeds by Artificial Neural Networks and Deep Reinforcement Learning: A Case Study in the City of Yaoundé TCHOUYIKBE BOURAI Zebulon Eric, BWEMBA Charles This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8057574/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 study focuses on the prototype of a traffic light control system to enhance signalized intersection coordination in urban environments using artificial intelligence techniques to analyze mean travel speeds. The system looks at integrate an artificial neural network for mean travel speeds prediction and a reinforcement learning model for speed-based traffic signal control. Field data were collected from Google Maps API for a road section in Yaoundé, Cameroon, namely the road axis joining the Messassi junction to the Nlongkak roundabout. The latter was used to simulate traffic conditions. It comes out that the speed regression neural network shows a high predictive accuracy with a mean square error of 1.095 and a R² of 0.908 for real speeds. The reinforcement learning traffic light control model, implemented in a simulation in the SUMO software and guided by rewards based solely on mean travel speeds, generated adaptive signal policies that outperformed fixed-cycle traffic light control systems. Simulation results revealed a reduction in the value of the average coefficient of variation of speeds—47.75% in one direction and 18.85% in the other, namely due to left-turns distribution along the axis and between directions—indicating improved coordination. These findings demonstrate the potential of AI-driven approaches combined with mean travel speeds as an alternative to the use of physical sensor-based data, to optimize urban traffic flow and reduce congestions. Civil Engineering Artificial Intelligence and Machine Learning Geographic Information Systems Urban mobility intersection coordination traffic signal optimization neural networks reinforcement learning Full Text Additional Declarations The authors declare no competing interests. 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. 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Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Urban mobility, intersection coordination, traffic signal optimization, neural networks, reinforcement learning\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8057574/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8057574/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study focuses on the prototype of a traffic light control system to enhance signalized intersection coordination in urban environments using artificial intelligence techniques to analyze mean travel speeds. The system looks at integrate an artificial neural network for mean travel speeds prediction and a reinforcement learning model for speed-based traffic signal control. Field data were collected from Google Maps API for a road section in Yaound\\u0026eacute;, Cameroon, namely the road axis joining the Messassi junction to the Nlongkak roundabout. The latter was used to simulate traffic conditions. It comes out that the speed regression neural network shows a high predictive accuracy with a mean square error of 1.095 and a R\\u0026sup2; of 0.908 for real speeds. The reinforcement learning traffic light control model, implemented in a simulation in the SUMO software and guided by rewards based solely on mean travel speeds, generated adaptive signal policies that outperformed fixed-cycle traffic light control systems. Simulation results revealed a reduction in the value of the average coefficient of variation of speeds\\u0026mdash;47.75% in one direction and 18.85% in the other, namely due to left-turns distribution along the axis and between directions\\u0026mdash;indicating improved coordination. These findings demonstrate the potential of AI-driven approaches combined with mean travel speeds as an alternative to the use of physical sensor-based data, to optimize urban traffic flow and reduce congestions.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Coordination of Urban Traffic Lights based on the Analysis of Mean Travel Speeds by Artificial Neural Networks and Deep Reinforcement Learning: A Case Study in the City of Yaoundé\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-10 03:26:59\",\"doi\":\"10.21203/rs.3.rs-8057574/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"ebe54a81-9e60-493b-855d-ef78df953557\",\"owner\":[],\"postedDate\":\"November 10th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":57619139,\"name\":\"Civil Engineering\"},{\"id\":57619140,\"name\":\"Artificial Intelligence and Machine Learning\"},{\"id\":57619141,\"name\":\"Geographic Information Systems\"}],\"tags\":[],\"updatedAt\":\"2025-11-10T03:26:59+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-10 03:26:59\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8057574\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8057574\",\"identity\":\"rs-8057574\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}