Abstract
In a modern Intelligent Transportation System (ITS), traffic prediction exerts an enormous function in alleviating traffic congestion and path planning. Due to the complex dynamic spatial-temporal dependence of traffic data, the traditional prediction methods have some limitations in the spatial-temporal correlation modeling, and cannot effectively predict both long-short term traffic conditions. In this paper, a novel deep learning framework Spatial-Temporal Graph Wavelet Attention Neural Network (ST-GWANN) is proposed for long-short term traffic prediction, which can comprehensively capture the spatial-temporal features. In the framework, the graph wavelet neural network and attention mechanism are integrated into the spatial gated block, which can obtain the spatial dependence of the road network. By combining the Gated Linear Units (GLU) and the temporal transformer layer, the local and global dependence of the time dimension are obtained. The proposed framework experimented on two real-world datasets, the results show that ST-GWANN outperforms state-of-art methods in traffic prediction tasks.
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Acknowledgment
This work is supported by National Key R&D Program of China (No.2017YFC0803300), the Beijing Natural Science Foundation (No.4192004), the National Natural Science of Foundation of China (No.61703013, 91646201, 62072016).
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Liu, Z., Ding, Z., Yang, B., Yuan, L., Li, L., Jia, N. (2021). ST-GWANN: A Novel Spatial-Temporal Graph Wavelet Attention Neural Network for Traffic Prediction. In: Pan, G., et al. Spatial Data and Intelligence. SpatialDI 2021. Lecture Notes in Computer Science(), vol 12753. Springer, Cham. https://doi.org/10.1007/978-3-030-85462-1_7
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