Traffic control system based on neuron networks

E. I. Chumachenko


The traffic control system based on neuron networks is considered. An accurate model of intersection is represented. It is determined the strategy of intersection traffic control using neural controller, which is based on classical neural network topologies of unidirectional multilayer perceptron


Traffic control system; neuron networks; unidirectional multilayer perceptron


Derrick H. Nguyen, Bernard Widrow, “Neural Networks for Self-Learning Control Systems”, IEEE Control Systems Magazine, April 1990.

Montgomery, D. C.; Runger G. C., Applied Statistics and Probability for Engineers, 2nd edition, John Wiley & Sons, Inc., 1999.

Roess, R. P.; William, W. R. and Prassas, E.S. Traffic Engineering, second edition, Prentice Hall, Upper Saddle River, New Jersey, 1998.

B. De Schutter and B. De Moor, “Optimal traffic light control for a single intersection,” European Journal of Control. vol. 4, no. 3, pp. 260–276, 1998.

B. De Schutter, “Optimal traffic light control for a single intersection,” Proceedings of the 1999 American Control Conference, San Diego, California, pp. 2195–2199, June 1999.

Jack Haddad, Bart De Schutter, David Mahalel, Ilya Ioslovich, and Per-Olof Gutman. IEEE Transactions on automatic control, vol. 55, no. 11, November 2010.

Johan, A. K. Suykens, Joos P. L. Vandewalle, Bart, L. R. De Moor. “Artificial NeuralNetworks for Moddeling and Control of Non-Linear Systems”, pp. 19–27, 28–30, 83–93.

Edited by Omid Omidvar, David L. Elliot, “Neural Systems for Control”, H. Ted Su, Tariq Samad, Neuro-Control Design: Optimization Aspects, pp. 259–283.

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