Freeway traffic flow modeling based on neural network

Freeway traffic flow modeling is the basis of control, analysis, design, and decision-making in intelligent transportation system. A traffic flow dynamic model using radial basis function neural network with feedback is studied. The fuzzy c-mean clustering algorithm was used to determine the position of centers of the hidden layer. A gradient descent method was used to obtain the weights from the hidden layer to the output layer. A freeway with four segments of same length, an on-ramp at the first segment, and an off-ramp at the third segment is discussed. The training data for traffic flow modeling were generated using a well-known macroscopic traffic flow model at different densities and average velocities. The simulation result proves the practicability of this algorithm.