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.
This work was supported in part by Grand 60373094 of National Natural Science Foundation of China and Grand 2002CB312003 of High Tech. Research and Development (973) Programme, China.
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Tan, G., Yuan, W. (2004). Traffic Flow Forecasting Based on Parallel Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_149
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DOI: https://doi.org/10.1007/978-3-540-28648-6_149
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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