| Title |
An Enhanced Motif Graph Clustering-Based Deep Learning Approach for Traffic Forecasting |
| ID_Doc |
44562 |
| Authors |
Zhang, CH; Zhang, SY; Yu, JJQ; Yu, S |
| Title |
An Enhanced Motif Graph Clustering-Based Deep Learning Approach for Traffic Forecasting |
| Year |
2020 |
| Published |
|
| DOI |
10.1109/GLOBECOM42002.2020.9322104 |
| Abstract |
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 graph-clustering, 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. |
| Author Keywords |
smart city; intelligent transportation system; graph clustering; traffic speed prediction |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000668970500049 |
| WoS Category |
Computer Science, Artificial Intelligence; Telecommunications |
| Research Area |
Computer Science; Telecommunications |
| PDF |
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