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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.
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.
Freeway traffic flow modeling based on neural network
Jian-Mei Xiao, (author) / Xi-Huai Wang, (author)
2003-01-01
230652 byte
Conference paper
Electronic Resource
English
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