ARTIFICIAL NEURAL NETWORK APPROACH TO TRAFFIC FLOW CONTROL

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Abstract

A 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.

About the authors

David E Kazaryan

Academy of Engineering Peoples’ Friendship University of Russia

Email: kazaryan.david@gmail.com
senior lecturer of department Mechanics and mechatronics Miklukho-Maklaya str., 6, Moscow, Russia, 117198

Vasiliy A Mihalyev

Academy of Engineering Peoples’ Friendship University of Russia

Email: vasiliy.mihalev@yandex.ru
PhD student, department Mechanics and mechatronics Miklukho-Maklaya str., 6, Moscow, Russia, 117198

Elena A Sofronova

Academy of Engineering Peoples’ Friendship University of Russia

candidate of technical sciences, deputy director for research, associate professor Mechanics and Mechatronics department Miklukho-Maklaya str., 6, Moscow, Russia, 117198

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Copyright (c) 2017 Kazaryan D.E., Mihalyev V.A., Sofronova E.A.

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