GraphSANet: A Graph Neural Network and Self Attention Based Approach for Spatial Temporal Prediction in Sensor Network

Traffic prediction has become increasingly hot in real-world applications. However, even though massive previous works have been conducted, traffic prediction based on the sensor is still confronted with unique challenges. In a nutshell, it is difficult for us to model both spatial dependency and temporal dependency. In this paper, we propose a novel model called GraphSANet which ensures both spatial and temporal dependencies are considered. With the usage of Temporal Self Attention, the temporal dependency could be captured perfectly and effectively, even if the problem caused by long-distance dependency could be alleviated. In the end, we conducted extensive experiments on two datasets, and vastly better prediction results prove the effectiveness of our model.