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| Title | An Urban Traffic Flow Fusion Network Based On A Causal Spatiotemporal Graph Convolution Network |
|---|---|
| ID_Doc | 9007 |
| Authors | Xu X.; Mao H.; Zhao Y.; Lü X. |
| Year | 2022 |
| Published | Applied Sciences (Switzerland), 12, 14 |
| DOI | http://dx.doi.org/10.3390/app12147010 |
| Abstract | Traffic flow prediction is an important part of intelligent transportation systems. In recent years, most methods have considered only the feature relationships of spatial dimensions of traffic flow data, and ignored the feature fusion of spatial and temporal aspects. Traffic flow has the features of periodicity, nonlinearity and complexity. There are many relatively isolated points in the nodes of traffic flow, resulting in the features usually being accompanied by high-frequency noise. The previous methods directly used the graph convolution network for feature extraction. A polynomial approximation graph convolution network is essentially a convolution operation to enhance the weight of high-frequency signals, which lead to excessive high-frequency noise and reduce prediction accuracy to a certain extent. In this paper, a deep learning framework is proposed for a causal gated low-pass graph convolution neural network (CGLGCN) for traffic flow prediction. The full convolution structure adopted by the causal convolution gated linear unit (C-GLU) extracts the time features of traffic flow to avoid the problem of long running time associated with recursive networks. The reduction of running parameters and running time greatly improved the efficiency of the model. The new graph convolution neural network with self-designed low-pass filter was able to extract spatial features, enhance the weight of low-frequency signal features, suppress the influence of high-frequency noise, extract the spatial features of each node more comprehensively, and improve the prediction accuracy of the framework. Several experiments were carried out on two real-world real data sets. Compared with the existing models, our model achieved better results for short-term and long-term prediction. © 2022 by the authors. |
| Author Keywords | deep learning; graph convolution network; smart city; traffic flow forecasting |
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