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Survey of neural network‐based models for short‐term traffic state prediction

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Traffic state prediction is a key component in intelligent transport systems (ITS) and has attracted much attention over the last few decades. Advances in computational power and availability of a large amount of data have paved the way to employ advanced neural network (NN) models for ITS, including deep architectures. There have been various NN‐based approaches proposed for short‐term traffic state prediction that are surveyed in this article, where the existing NN models are classified and their application to this area is reviewed. An in‐depth discussion is provided to demonstrate how different types of NNs have been used for different aspects of short‐term traffic state prediction. Finally, possible further research directions are suggested for additional applications of NN models, especially using deep architectures, to address the dynamic nature in complex transportation networks.

This article is categorized under:

  • Technologies > Prediction
  • Technologies > Machine Learning
  • Application Areas > Science and Technology
Radial basis function neural network
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A multilayer feedforward neural network with one hidden layer
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The snapshot frequency of different short‐term traffic state forecasting methods found in the literature. Note: The values on this figure are estimated by searching the relevant keywords in titles using Google Scholar on February 12, 2018
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An example of a fuzzy neural network
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A deep belief network design
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Structure of a restricted Boltzmann machine
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A convolutional neural network model
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A gated recurrent unit neural network. (a) Gated recurrent unit and (b) GRU network
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– A long‐short term memory neural network. (a) Long short‐term memory unit and (b) LSTM network
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Recurrent neural networks. (a) RNN with self‐feedback at the hidden layer and (b) RNN with feedback from the output layer to the hidden layer
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An example design of a time‐delay neural network
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Wavelet neural network
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Technologies > Machine Learning
Technologies > Prediction
Application Areas > Science and Technology

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