Abstract
Freeway traffic flow is a nonlinear and time-variant system, the application of radial basis function (RBF) neural network to freeway macroscopic traffic flow dynamic modeling is presented. A learning algorithm of subtractive clustering method is used to obtain the parameters of radial basis function in this paper, so that RBF neural network has an optimized structure. The simulation results show the algorithm is effective for freeway traffic flow modeling.
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