NDLI: Forecasting Railway Network Data Traffic: A Model and a Neural Network Solution Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Teng Jing
Copyright Year	2008
Description	Author affiliation: Sch. of Transp. Eng., Tongji Univ., Shanghai (Teng Jing)
Abstract	Forecasting network data traffic is an important part of the function of planning and managing information systems. However, the contents of network data are so stochastic and complex that it is very difficult to establish stable functions to describe the mapping relationship between data flows and associated causal influences. In this paper, a multi-layer feed forward neural networks (NN) model is put forward to identify such relationship and the corresponding learning rule of NN, back-propagation (BP) algorithm, is given. In addition necessary estimation and validation processes are designed to ensure the successful implementation of the model proposed. The paper elucidates the application of NN model around the case of forecasting China railway Transportation Management Information Systems (TMIS) network traffic. The predictive results obtained demonstrate that the NN model and the solution algorithm are applicable for information planning on the TMIS network.
Starting Page	203
Ending Page	208
File Size	210631
Page Count	6
File Format	PDF
ISBN	9780769533421
DOI	10.1109/PEITS.2008.23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Network data traffic TMIS Neural networks Transportation Back-propagation algorithm Artificial neural networks Predictive models Rail transportation Planning Forecasting
Content Type	Text
Learning Resource Type	Article

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