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Sensors 2017, 17(7), 1501; doi:10.3390/s17071501

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

1
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China
2
Passenger Vehicle EE Development Department, China FAW R&D Center, Changchun 130011, China
*
Author to whom correspondence should be addressed.
Received: 4 May 2017 / Revised: 17 June 2017 / Accepted: 21 June 2017 / Published: 26 June 2017
(This article belongs to the Section Sensor Networks)
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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. View Full-Text
Keywords: traffic prediction; convolutional neural network; long short-term memory; spatiotemporal feature; network representation traffic prediction; convolutional neural network; long short-term memory; spatiotemporal feature; network representation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yu, H.; Wu, Z.; Wang, S.; Wang, Y.; Ma, X. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks. Sensors 2017, 17, 1501.

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