Variational spatial-temporal graph attention network for state monitoring and forecasting

With the popularity of smart devices, there has been extensive collection of data structured in graphs. Spatial graphs, in particular, have garnered significant interest owing to their diverse range of applications. In this paper, we focus on applying spatial graph model for state monitoring and forecasting, with special attention to transportation systems. In current spatial modeling approaches, understanding the structure of a spatial graph usually relies on the analysis of gathered spatial-temporal data. However, spatial graph data in real world often contains temporary factors that are not easily detected. In this paper, we propose a novel variational inference-based model VISTG, which integrates such dynamics into spatial-temporal learning of spatial graph modeling. Specifically, VISTG is primarily composed of several spatial-temporal learning blocks, each encompassing both temporal and spatial learning layers. The temporal learning layer is crafted to characterize the distributions of latent factors using a variational inference-based model, aiming to capture the dynamics within the data. Subsequently, the spatial learning layer utilizes graph attention networks to describe the correlation among nodes. Additionally, an adaptive fusion module is implemented to equalize the impact of diverse temporal patterns. Finally, comprehensive experiments are carried out on two real-world datasets. The results affirm the efficacy of our model.