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

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