DocumentCode :
2944251
Title :
Research on Method of the Subsection Learning of Double-Layers BP Neural Network in Prediction of Traffic Volume
Author :
Mao, Yuming ; Shi, Shiying
Author_Institution :
Dept. of Inf. Eng., Shandong Jiaotong Univ., Jinan, China
Volume :
3
fYear :
2009
fDate :
11-12 April 2009
Firstpage :
294
Lastpage :
297
Abstract :
Prediction of traffic volume is the key technology in intelligent transportation systems. BP neural network is universally used in prediction of traffic volume. This research aimed at advancing BP neural networkpsilas precision in prediction of traffic flow. The method of prediction of traffic volume was based on the subsection learning of double-layers BP neural network. The improved method was used to predict the traffic volume of Jingshi road Jinan city, then compared the results maked by subsection-learning method and by common method. Using subsection-learning method,the average relative tolerance was decreased by 2.52%. The improved BP neural network can be used to predict the traffic volume.
Keywords :
automated highways; backpropagation; neural nets; traffic engineering computing; double-layer BP neural network; intelligent transportation system; subsection learning; traffic volume prediction; Artificial neural networks; Autoregressive processes; Communication system traffic control; Intelligent networks; Intelligent transportation systems; Neural networks; Neurons; Predictive models; Telecommunication traffic; Traffic control; BP neural network; prediction; traffic volume;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-0-7695-3583-8
Type :
conf
DOI :
10.1109/ICMTMA.2009.642
Filename :
5203204
Link To Document :
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