Model predictive control for urban traffic flows

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 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 obtained good parameters of the network model. © 2016 IEEE.

Authors
Publisher
Institute of Electrical and Electronics Engineers Inc.
Language
English
Pages
3051-3056
Status
Published
Number
7844705
Year
2017
Organizations
  • 1 Peoples' Friendship University of Russia, Cybernetics and Mechatronics Department, Moscow, Russian Federation
  • 2 Federal Research Center Computer Science and Control, Russia Academy of Sciences, Russian Federation
Keywords
Computer software; Cybernetics; Differential equations; Model predictive control; Motor transportation; Nonlinear equations; Pollution control; Roads and streets; Traffic control; Transportation; Active phasis; Connection matrices; Input and outputs; Microscopic traffic simulation; Network modeling; Network reinforcements; Traffic flow control; Urban traffic flow; Street traffic control
Date of creation
19.10.2018
Date of change
19.10.2018
Short link
https://repository.rudn.ru/en/records/article/record/5624/