Title | ||
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High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. |
Abstract | ||
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Traffic forecasting is a challenging task, due to the complicated spatial dependencies on roadway networks and the time-varying traffic patterns. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, High-Order Graph Convolutional Long Short-Term Memory Neural Network (HGC-LSTM), to learn the interactions between links in the traffic network and forecast the network-wide traffic state. We define the high-order traffic graph convolution based on the physical network topology. The proposed framework employs L1-norms on the graph convolution weights and L2-norms on the graph convolution features to identify the most influential links in the traffic network. We propose a novel Real-Time Branching Learning (RTBL) algorithm for the HGC-LSTM framework to accelerate the training process for spatio-temporal data. Experiments show that our HGC-LSTM network is able to capture the complex spatio-temporal dependencies efficiently present in the traffic network and consistently outperforms state-of-the-art baseline methods on two heterogeneous real-world traffic datasets. The visualization of graph convolution weights shows that the proposed framework can accurately recognize the most influential roadway segments in real-world traffic networks. |
Year | DOI | Venue |
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2018 | 10.1109/tits.2019.2950416 | arXiv: Learning |
Field | DocType | Volume |
Graph,Visualization,Convolution,Recurrent neural network,Artificial intelligence,Traffic network,Deep learning,Artificial neural network,Machine learning,Mathematics,Branching (version control) | Journal | abs/1802.07007 |
Citations | PageRank | References |
9 | 0.49 | 13 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhiyong Cui | 1 | 16 | 1.39 |
Kristian Henrickson | 2 | 9 | 0.49 |
Ke Ruimin | 3 | 89 | 6.69 |
Yinhai Wang | 4 | 9 | 0.49 |