Journal
SENSORS
Volume 17, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/s17071501
Keywords
traffic prediction; convolutional neural network; long short-term memory; spatiotemporal feature; network representation
Funding
- National Natural Science Foundation of China [51308021, 51408019, U1564212]
- National Science and Technology Support Program of China [2014BAG01B02]
- Beijing Nova Program [z151100000315048]
- Beijing Natural Science Foundation [9172011]
- Young Elite Scientist Sponsorship Program by the China Association for Science and Technology [2016QNRC001]
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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.
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