NDLI: Freeway traffic flow modeling based on neural network

Content Provider	IEEE Xplore Digital Library
Author	Jian-Mei Xiao Xi-Huai Wang
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng. & Autom., Shanghai Maritime Univ., China (Jian-Mei Xiao; Xi-Huai Wang)
Abstract	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.
Starting Page	136
Ending Page	139
File Size	230652
Page Count	4
File Format	PDF
ISBN	0780381254
DOI	10.1109/ITSC.2003.1251935
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Traffic control Telecommunication traffic Neural networks Communication system traffic control Intelligent transportation systems Control system analysis Decision making Radial basis function networks Neurofeedback Clustering algorithms
Content Type	Text
Resource Type	Article

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