MAGCN: A Multi-Adaptive Graph Convolutional Network for Traffic Forecasting

Traffic forecasting is one of the most fundamental components in many applications from urban computing to intelligent transportation. Recently, graph convolutional networks (GCNs), which model traffic data as spatiotemporal graph, have attracted lots of attention. However, existing GCNs mainly focus on pre-defined graph structure which is fixed over the entire network. These methods of traffic forecasting can not capture the complex spatial correlations especially for the higher level features. To address these problems, we propose a novel Multi-Adaptive Graph Convolutional Network (MAGCN) for traffic forecasting in this work. Our model can dynamically learn the topology of the graph through multi-range GCNs in an end-to-end manner. This data-driven method makes the construction of graph more flexible and increases the generality of model to adapt to various data samples. Moreover, in the proposed framework, we design a novel Differential Temporal Graph Convolutional Network (DTGCN) to capture the periodic and immediate temporal correlations of traffic data, which is integrated in MAGCN to effectively capture the dynamic spatial-temporal dependencies. Besides, we adopt a multi-subgraph encoding mechanism to enhance the representation of complex spatial dependency. Extensive experiments on two real-world datasets demonstrate that the performance of our MAGCN exceeds the state-of-the-art baselines.