Abstract: Traffic flow prediction plays an important role in intelligent transportation systems(ITS).The key challenge in traffic flow prediction is to efficiently and comprehensively extract the complex spatiotemporal correlations in cities.Traffic speed has not only short-term and long-term periodic dependencies in the temporal dimension,but also local and global dependencies in the spatial dimension.Existing methods have certain limitations in capturing the spatiotemporal dependencies of traffic data.To this end,this paper proposes a deep learning model based on the global spatialtemporal graph convolutional network(GSTGCN) to address the limitations of urban traffic speed prediction.There are three spatiotemporal components in the model,which can model the three different spatiotemporal correlations in traffic data,namely,recent,daily,and weekly cycles.Each spatiotemporal component consists of a time module and a spatial module.In order to better obtain the temporal dimension information of traffic data,the time module introduces the Informer mechanism to adaptively assign feature weights.In order to better obtain the spatial relationship of traffic data,the spatial model introduces a graph convolutional neural network to extract local and global spatial information of traffic data.In the experiments,the proposed model is tested on two different real-world datasets.The results show that the proposed GSTGCN outperforms the most advanced baseline models.

Key words: Traffic flow prediction, Global spatiotemporal graph convolutional neural network, spatiotemporal dependence