RUDN Journal of Engineering ResearchRUDN Journal of Engineering ResearchВестник Российского университета дружбы народов. Серия: Инженерные исследования2312-81432312-8151Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)1600410.22363/2312-8143-2017-18-1-97-106CYBERNETICS AND MECHATRONICSИнформатика, вычислительная техника и управлениеResearch ArticleARTIFICIAL NEURAL NETWORK APPROACH TO TRAFFIC FLOW CONTROLНЕЙРОСЕТЕВЫЕ ПОДХОДЫ К УПРАВЛЕНИЮ ПОТОКАМИ ТРАНСПОРТАKazaryanDavid EКазарянДавид Эдуардовичsenior lecturer of department Mechanics and mechatronicsстарший преподаватель департамента механики и мехатроникиkazaryan.david@gmail.comMihalyevVasiliy AМихалевВасилий АндреевичPhD student, department Mechanics and mechatronicsаспирант департамента механики и мехатроникиvasiliy.mihalev@yandex.ruSofronovaElena AСофроноваЕлена Анатольевнаcandidate of technical sciences, deputy director for research, associate professor Mechanics and Mechatronics departmentкандидат технических наук, заместитель директора по научной работе, доцент департамента механики и мехатроники-Academy of Engineering Peoples’ Friendship University of RussiaИнженерная академия Российский университет дружбы народов15122017181VOL 18, NO1 (2017)ТОМ 18, №1 (2017)9710613052017Copyright ©; 2017, Kazaryan D.E., Mihalyev V.A., Sofronova E.A.Copyright ©; 2017, Казарян Д.Э., Михалев В.А., Софронова Е.А.2017Kazaryan D.E., Mihalyev V.A., Sofronova E.A.Казарян Д.Э., Михалев В.А., Софронова Е.А.http://creativecommons.org/licenses/by/4.0https://journals.rudn.ru/engineering-researches/article/view/16004A 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.В работе рассматривается задача оптимального управления транспортными потоками. Представлена математическая модель для управления сигналами светофоров на основе теории управляемых сетей в виде системы нелинейных конечномерно-дифференцируемых уравнений. Для представления сети дорог большого размера в модели используются матрицы связей, которые описывают связи между входными и выходными участками подсетей. Управление транспортным потоком достигается за счет изменения активных фаз светофоров. Задачей управления является минимизация разницы между суммарным входным и выходным потоками всех подсетей. В статье представлен нейросетевой подход для корректировки параметров сети дорог.traffic flow controlartificial neural networksуправление транспортными потокамиискусственные нейронные сети1.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. A., vol. 229, № 1178, pp. 317-345, 1995.2.V. Mauro. “Road Network Control”. In M. 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Proc. of 17th International Conference on Intelligent Transportation Systems 2014 (ITSC) 8-11 October, 2014, Quingdao, China, pp. 2144-2149.17.W. Genders, S. Razavi. Using a deep reinforcement learning agent for traffic signal control.Submited to IEEE for publication on 3 November 2016.