SCFNet:Fusion Framework of External Spatial Features for Spatio-temporal Prediction

Road information is closely related to the current traffic pattern of roads.Rich POI semantics can reveal the attributes of an area.Demographic data reveals the trend of population flow in an area.Considering the influence brought by the above external spatial features on the flow in spatio-temporal prediction can help the model accomplish more accurate prediction.Existing external spatial modeling methods usually focus on the input external spatial features,learn spatially relevant semantic representations through neural network mapping,and then fuse them with the final spatiotemporal flow representations.However,due to the heterogeneity between flow representations and spatial features,the existing external spatial feature modeling methods are often not highly scalable and can only target specific external spatial features or specific spatio-temporal models.To overcome the above problems,we propose a spatial context fusion network for traffic forecasting(SCFNet).Specifically,we introduce an attention mechanism based on information interaction to compute attention scores between spatio-temporal representations and external spatial features to achieve an efficient fusion of external spatial features and spatio-temporal representations,and we design a dynamic encoding method of time vectors to generate dynamic spatial feature semantics.SCFNet supports a mixture of different spatial static features such as regional demographic data,road information,and POI inputs.We conduct experiments on three real traffic datasets and demonstrated that SCFNet can significantly improve the prediction accuracy of various state-of-the-art spatiotemporal prediction methods such as(MTGNN,ASTGCN,and GraphWaveNet).