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Most of the existing algorithms for traffic speed forecasting split spatialfeatures and temporal features to independent modules, and then associateinformation from both dimensions. However, features from spatial and temporaldimensions influence mutually, separated extractions isolate such dependencies,and might lead to inaccurate results. In this paper, we incorporate theperspective of information diffusion to model spatial features and temporalfeatures synchronously. Intuitively, vertices not only diffuse information tothe neighborhood but also to the subsequent state along with the temporaldimension. Therefore, we can model such heterogeneous spatial-temporalstructures as a homogeneous process of diffusion. On this basis, we propose anIterative Spatial-Temporal Diffusion Graph Convolutional Network (ISTD-GCN) toextract spatial and temporal features synchronously, thus dependencies betweenboth dimensions can be better modeled. Experiments on two traffic datasetsillustrate that our ISTD-GCN outperforms 10 baselines in traffic speedforecasting tasks. The source code is available athttps://github.com/Anonymous.

ISTD-GCN: Iterative Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Speed Forecasting