DocumentCode
131705
Title
Study on Traffic Flow Base on RBF Neural Network
Author
Xiaoying Li
Author_Institution
Coll. of Electr. & Inf. Eng., Changsha Univ. of Sci. & Technol., Changsha, China
fYear
2014
fDate
10-11 Jan. 2014
Firstpage
645
Lastpage
647
Abstract
According to charging lathing standard type classification volume of traffic blowing the expense at enormous and the data is inaccurate, But the volume of traffic may obtain easily according to the traffic survey classification from each kind of intelligent toll station. The function neural networks method will charge with because of radial base the rate of flow data change becomes volume of traffic inquiring into a data, Building-up changes a model, Using the MATLAB programming, Obtaining each kind of vehicle type error distinguish ratio and total error distinguish ratio. It can utilize fully thereby advantage of the fee-collecting station, Every fee-collecting station all has the precise writer that various motorcycle type passes in the process charging, it can cut down the cost getting the traffic survey data, Enables the traffic survey the data to obtain the full use.
Keywords
pattern classification; radial basis function networks; road traffic; traffic engineering computing; Matlab programming; RBF neural network; fee-collecting station; intelligent toll station; lathing standard type classification volume; motorcycle type; radial basis function neural network; total error distinguish ratio; traffic flow; traffic survey classification; traffic volume; vehicle type error distinguish ratio; Educational institutions; MATLAB; Mathematical model; Presses; Radial basis function networks; Training; Vehicles; RBF neural network; error ratio; toll system; traffic flow;
fLanguage
English
Publisher
ieee
Conference_Titel
Measuring Technology and Mechatronics Automation (ICMTMA), 2014 Sixth International Conference on
Conference_Location
Zhangjiajie
Print_ISBN
978-1-4799-3434-8
Type
conf
DOI
10.1109/ICMTMA.2014.159
Filename
6802776
Link To Document