A pagerank self-attention network for traffic flow prediction

Traffic information is collected from sensors in the urban road network, and traffic information can be said to be a mapping of people’s activities, which are difficult to model as a linear function, so this makes traffic information difficult to be predicted. In other words, traffic information is difficult to build effective models to predict traffic information because of its non-linear characteristics that are difficult to capture. As researchers go deeper, researchers have been able to extract good spatio-temporal features for modern urban road networks. However, it is worth mentioning that most researchers have neglected the importance of models for global potential features under the topology map of urban road networks, yet this global potential feature is very important for traffic prediction. In this paper, we propose a new spatio-temporal graph convolutional network model A Pagerank Self-attention Network (hereafter we abbreviate as PSN) in order to solve this problem based on a full consideration of the urban road network topology features, in which we employ a global spatio-temporal self-attention module to capture the global spatio-temporal features well. and the graph wandering module is used to propagate the spatio-temporal feature information effectively and widely. It is worth mentioning that experiments on two well-known datasets show that our proposed method achieves better prediction results compared to existing baseline methods.