Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting

• URL: http://arxiv.org/abs/2206.03128v1
• Date: Tue, 7 Jun 2022 09:08:35 GMT
• Title: Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting
• Authors: Chen Weikang and Li Yawen and Xue Zhe and Li Ang and Wu Guobin
• Abstract summary: We propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes. Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block.
• Score: 4.1700160312787125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic forecasting is one canonical example of spatial-temporal learning task in Intelligent Traffic System. Existing approaches capture spatial dependency with a pre-determined matrix in graph convolution neural operators. However, the explicit graph structure losses some hidden representations of relationships among nodes. Furthermore, traditional graph convolution neural operators cannot aggregate long-range nodes on the graph. To overcome these limits, we propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes. Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block. What's more, a stacked dilated 1D convolution, with efficiency in long-term prediction, is adopted in our temporal block for capturing the different time series. We evaluate our STAAN on two real-world datasets, and experiments validate that our model outperforms state-of-the-art baselines.

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