Interactive dynamic diffusion graph convolutional network for traffic flow prediction

Capturing the temporal and spatial features, and the spatiotemporal correlations in traffic networks is the essential task for accurate traffic flow prediction. Although most existing models have explored the temporal features deeply, the periodicity and dynamics of diffusion signals in spatial features are neglected. Besides, most previous methods cannot capture the heterogeneous spatiotemporal correlations of traffic sequences. In order to tackle the above two issues, a novel deep learning -based model called interactive dynamic diffusion graph convolutional network is proposed to realize accurate traffic flow prediction. Firstly, a new periodic and dynamic diffusion graph convolutional network is proposed to extract the periodicity and dynamics of diffusion signals in spatial features. Secondly, a new spatiotemporal interactive dynamic graph generator is proposed to generate a spatiotemporal dynamic graph through a spatiotemporal interactive operation, and it can comprehensively capture the heterogeneous spatiotemporal correlations of traffic sequences. Finally, the proposed model attains an accurate traffic flow prediction through extensive experiments on two real -world traffic flow datasets, which confirm the superiority of the proposed model compared with twelve baseline models.