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.

David E Kazaryan
Academy of Engineering Peoples’ Friendship University of Russia
Miklukho-Maklaya str., 6, Moscow, Russia, 117198

senior lecturer of department Mechanics and mechatronics

Vasiliy A Mihalyev
Academy of Engineering Peoples’ Friendship University of Russia
Miklukho-Maklaya str., 6, Moscow, Russia, 117198

PhD student, department Mechanics and mechatronics

Elena A Sofronova

Academy of Engineering Peoples’ Friendship University of Russia
Miklukho-Maklaya str., 6, Moscow, Russia, 117198

candidate of technical sciences, deputy director for research, associate professor Mechanics and Mechatronics department

  • 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.
  • V. Mauro. “Road Network Control”. In M. Papageorgious, editor, Concise Encyclopedia of Traffic and transportation Systems. Advanced in Systems, Control in Information Engineering, pp. 361-366. Pergamon Press, 1991.
  • S.A. Ardekani and R. Herman. “Urban Network-Wide Variables and Their Relations”, Transportation Science, vol. 21, № 1, 1987.
  • A.A. Assad. “Multicommodity network flows - a survey”, Networks, vol. 8, № 1, pp. 37-91, 1978.
  • T. Peter. “Modeling nonlinear road traffic networks for junction control”, Int. J. of Applied Mathematics and Computer Sciences, 2012, vol. 22, No. 3, pp. 723-732.
  • K.-H. Chao, R.-H. Lee, M.-H. Wang “An Intelligent Traffic Light Control Based on Extension Neural Network” Proceedings 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, Part I. pp. 17-24.
  • J. Hu, D. Zhao, F. Zhu “Neural network based online traffic signal controller design with reinforcement training” Proc. 14th International IEEE Conference on Intelligent Transportation Systems (ITSC). 5-7 Oct. 2011. Pp. 1045-1060.
  • A.I. Diveev. “Controlled networks and their applications”, Computational Mathematics and Mathematical Physics, vol. 48, № 8, pp. 1428-1442, 2008.
  • G.H.A. Alnovani, A.I. Diveev, K.A Pupkov and E.A. Sofronova. “Control Synthesis for Traffic Simulation in the Urban Road Network”. Proc. of the 18th IFAC World Congress, Milano, Italy August 28 - September 2, 2011, pp. 2196-2201.
  • A.I. Diveev and E.A. Sofronova. “Synthesis of Intelligent Control of Traffic Flows in Urban Roads Based on the Logical Network Operator Method”, Proceedings of European Control Conference (ECC-2013) July 17-19, 2013, Zürich, Switzerland, pp. 3512-3517.
  • A. Diveev, E. Sofronova, V. Mikhalev 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings 7844705, pp. 3051-3056.
  • Tu, Y.L., W.J. Zhang, X.Liu., W. Li, C-L. Chai, Ralph Deters et al., 2008. A disaster response management system based on control systems technology, Int. J. of Critical Infrastructures, 4(3), pp. 274-285.
  • J.W. Wang, W.H. Ip, W.J. Zhang. An integrated road construction and resource planning approach to the evacuation of victims from single source to multiple destinations. Intelligent Transportation Systems, IEEE Transactions on 11 (2), 277-289.
  • J.C. Chedjou, K. Kyamakya. Cellular neural networks based local traffic signals control at a junction/intersection. Proceedings of the 1st IFAC Conference on Embedded Systems 2012 (CESCIT-2012) 3-5 April, 2012, Wurzburg, Germany, pp. 81-85.
  • S. Araghi, A. Khosravi, D. Creighton. Optimal design of traffic signal controller, using neural networks and fuzzy logic systems. Proceedings of the International Joint Conference on Neural Networks 2014 (IJCNN) 6-11 July, 2014, Beijing, China, pp. 42-47.
  • G.B. Castro, J.C. Martini, A. Hirakawa. Biologically-inspired neural network for traffic signal control. Proc. of 17th International Conference on Intelligent Transportation Systems 2014 (ITSC) 8-11 October, 2014, Quingdao, China, pp. 2144-2149.
  • W. Genders, S. Razavi. Using a deep reinforcement learning agent for traffic signal control.Submited to IEEE for publication on 3 November 2016.


Abstract - 56

PDF (Russian) - 44

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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.