RUDN Journal of Engineering ResearchRUDN Journal of Engineering Research2312-81432312-8151Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)1600410.22363/2312-8143-2017-18-1-97-106Research ArticleARTIFICIAL NEURAL NETWORK APPROACH TO TRAFFIC FLOW CONTROLKazaryanDavid Esenior lecturer of department Mechanics and mechatronicskazaryan.david@gmail.comMihalyevVasiliy APhD student, department Mechanics and mechatronicsvasiliy.mihalev@yandex.ruSofronovaElena Acandidate of technical sciences, deputy director for research, associate professor Mechanics and Mechatronics department-Academy of Engineering Peoples’ Friendship University of Russia151220171819710613052017Copyright © 2017, Kazaryan D.E., Mihalyev V.A., Sofronova E.A.2017A problem of optimal urban traffic flows control is considered. A mathematical model of control by the traffic lights at intersections using the controlled networks theory is given. It is a system of nonlinear finite-differential equations. To present a large scale road networks the model contains the connection matrices that describe interactions between input and output roads in subnetworks. The traffic flow control is performed by the coordination of active phases of traffic lights. A control goal is to minimize the difference between the total input flow and total output flow for all subnetworks. In this paper, a neural network approach for urban traffic road network parameters adjustment is presented. A simulation is conducted under a microscopic traffic simulation software CTraf. Results demonstrate that neural network reinforcement training obtain good parameters of the network model.traffic flow controlartificial neural networksуправление транспортными потокамиискусственные нейронные сети[M.J. Ligthill and F.R.S. Whitham. “On kinetic waves II. A theory of traffic flow on crowded roads”, Proc. of the Royal Society Ser. 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