Spatiotemporal Residual Graph Attention Network for Traffic Flow Forecasting

Accurate spatiotemporal traffic flow forecasting is significant for the modern traffic management and control. In order to capture the spatiotemporal characteristics of the traffic flow simultaneously, we propose a novel spatiotemporal residual graph attention network (STRGAT). First, the network adopts a deep full residual graph attention block, which performs a dynamic aggregation of spatial features regarding the node information of the traffic network. Second, a sequence-to-sequence block is designed to capture the temporal dependence in the traffic flow. The traffic flow data with weekly periodic dependencies are also integrated and STRGAT is used for traffic forecasting of traffic road networks. The experiments are conducted on three real data sets in California, USA. Results verify that our proposed STRGAT is able to learn the spatiotemporal correlation of traffic flow well and outperforms the state-of-the-art methods.

Automated summary

Accurate spatiotemporal traffic flow forecasting is crucial for modern traffic management and control. To capture the spatiotemporal characteristics of traffic flow, a novel spatiotemporal residual graph attention network (STRGAT) is proposed. It adopts a deep full residual graph attention block to aggregate spatial features and a sequence-to-sequence block to capture temporal dependence. Experiments on real datasets in California show that STRGAT outperforms state-of-the-art methods in learning the spatiotemporal correlation of traffic flow.