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dc.creatoramelec, viloria
dc.creatorPineda, Omar
dc.creatorVarela Izquierdo, Noel
dc.creatorDiaz Martínez, Jorge Luis
dc.date.accessioned2021-03-17T17:13:10Z
dc.date.available2021-03-17T17:13:10Z
dc.date.issued2020-07-19
dc.identifier.issn18650929
dc.identifier.urihttps://hdl.handle.net/11323/8037
dc.descriptionRetractedspa
dc.description.abstractWith the excessive growth of modern cities, great problems are generated in citizen administration. One of these problems is the control of vehicle flow during peak hours. This paper proposes a solution to the problem of vehicle control through a proactive approach based on Machine Learning. Through this solution, a traffic control system learns about traffic flow in order to prevent future problems of long queues at traffic lights. The architecture of the traffic system is based on the principles of Autonomous Computing with the aim of changing the traffic light timers automatically. A simulation of the roads in an intelligent city and a Weka-based tool were created to validate this approach.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceCommunications in Computer and Information Sciencespa
dc.subjectMachine Learningspa
dc.subjectProactive controlspa
dc.subjectTrafficspa
dc.subjectSmart citiesspa
dc.subjectAutonomous Computingspa
dc.titleOptimization of driving efficiency for pre-determined routes: proactive vehicle traffic controlspa
dc.typePreprintspa
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dc.source.urlhttps://link.springer.com/chapter/10.1007/978-981-15-6648-6_7spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-6648-6_7
dc.type.hasversioninfo:eu-repo/semantics/draftspa


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