Abstract
Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.
Highlights
Predicting large-scale network-wide traffic is a vital and challenging topic for transportation researchers
Sadek applied a multilayer perceptron (MLP) and a fuzzy neural network (FNN) to high-speed network traffic prediction; the results indicate that neural networks (NNs) performs better than the autoregressive model [30]
Inspired by the research findings of motion prediction in the domain of computer vision, where the future movement of an object can be estimated from a sequence of scenes generated by the same object, we proposed a novel grid-based transportation network segmentation method
Summary
Predicting large-scale network-wide traffic is a vital and challenging topic for transportation researchers. To learn time series with long time spans, long short-term memory (LSTM) neural networks (NNs), which were proposed by Hochreiter and Schmidhuber [10] in 1997, have been effectively applied in short-term traffic prediction [11,12] and achieve a remarkable performance in capturing the long-term temporal dependency of traffic flow. Motivated by the success of CNNs and LSTMs, this paper proposes a spatiotemporal image-based approach to predict the network-wide traffic state using spatiotemporal recurrent convolutional networks (SRCNs). In the proposed SRCN architecture, the DCNNs capture the near- and far-side spatial dependencies from the perspective of the network, whereas the LSTMs learn the long-term temporal dependency. At the end of this paper, the conclusions are presented and future studies are discussed
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