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Fu et al., 2016 - Google Patents

Using LSTM and GRU neural network methods for traffic flow prediction

Fu et al., 2016

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Document ID
4494688838498470636
Author
Fu R
Zhang Z
Li L
Publication year
Publication venue
2016 31st Youth academic annual conference of Chinese association of automation (YAC)

External Links

Snippet

Accurate and real-time traffic flow prediction is important in Intelligent Transportation System (ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear models and cannot describe the stochastic and nonlinear nature of traffic flow. In recent …
Continue reading at www.researchgate.net (PDF) (other versions)

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
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    • G—PHYSICS
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    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
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    • G—PHYSICS
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    • G06N5/00—Computer systems utilising knowledge based models
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