A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

• URL: http://arxiv.org/abs/2006.11583v1
• Date: Sat, 20 Jun 2020 14:12:01 GMT
• Title: A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting
• Authors: Jiawei Zhu, Yujiao Song, Ling Zhao and Haifeng Li
• Abstract summary: An attention temporal graph convolutional network (A3T-GCN) traffic forecasting method was proposed to capture global temporal dynamics and spatial correlations. The A3T-GCN model learns the short-time trend in time series by using the gated recurrent units and learns the spatial dependence based on the topology of the road network. Experimental results in real-world datasets demonstrate the effectiveness and robustness of proposed A3T-GCN.
• Score: 4.147625439377302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In terms of the temporal factor, although there exists a tendency among adjacent time points in general, the importance of distant past points is not necessarily smaller than that of recent past points since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) traffic forecasting method was proposed to simultaneously capture global temporal dynamics and spatial correlations. The A3T-GCN model learns the short-time trend in time series by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of proposed A3T-GCN. The source code can be visited at https://github.com/lehaifeng/T-GCN/A3T.

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