An Enhanced Motif Graph Clustering-Based Deep Learning Approach for Traffic Forecasting
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings, 2020, 00, pp. 1-6
- Issue Date:
- 2020-12-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| An Enhanced Motif Graph.pdf | Published version | 675.21 kB |
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Traffic speed prediction is among the key problems in intelligent transportation system (ITS). Traffic patterns with complex spatial dependency make accurate prediction on traffic networks a challenging task. Recently, a deep learning approach named Spatio-Temporal Graph Convolutional Networks (STGCN) has achieved state-of-the-art results in traffic speed prediction by jointly exploiting the spatial and temporal features of traffic data. Nonetheless, applying STGCN to large-scale urban traffic network may develop degenerated results, which is due to redundant spatial information engaging in graph convolution. In this work, we propose a motif-based graph-clustering approach to apply STGCN to large-scale traffic networks. By using graphclustering, we partition a large urban traffic network into smaller clusters to prompt the learning effect of graph convolution. The proposed approach is evaluated on two real-world datasets and is compared with its variants and baseline methods. The results show that graph-clustering approaches generally outperform the other methods, and the proposed approach obtains the best performance.
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