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Indexed by:会议论文

Date of Publication:2004-08-19

Included Journals:EI

Volume:3174

Page Number:937-942

Abstract:In Intelligent Transportation Systems (ITS), traffic flow forecasting is important to Traffic Flow Guidance System (TFGS). However most of the traffic flow forecasting models cannot meet the requirement of TFGS. This paper presents a traffic flow forecasting model based on BP neural network according to the correlation theory. This model greatly reduces the size of input patterns. Meanwhile, a new parallel training algorithm based on training set decomposition is presented. This algorithm greatly reduces the communication cost. Experiment results show that the new algorithm converges faster than traditional one, and can meet practical requirement. © Springer-Verlag Berlin Heidelberg 2004. All rights reserved.