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The prediction of urban vehicle flow and speed can greatly facilitatepeople's travel, and also can provide reasonable advice for the decision-makingof relevant government departments. However, due to the spatial, temporal andhierarchy of vehicle flow and many influencing factors such as weather, it isdifficult to prediction. Most of the existing research methods are to extractspatial structure information on the road network and extract time seriesinformation from the historical data. However, when extracting spatialfeatures, these methods have higher time and space complexity, and incorporatea lot of noise. It is difficult to apply on large graphs, and only considersthe influence of surrounding connected road nodes on the central node, ignoringa very important hierarchical relationship, namely, similar information ofsimilar node features and road network structures. In response to theseproblems, this paper proposes the Graph Hierarchical Convolutional RecurrentNeural Network (GHCRNN) model. The model uses GCN (Graph ConvolutionalNetworks) to extract spatial feature, GRU (Gated Recurrent Units) to extracttemporal feature, and uses the learnable Pooling to extract hierarchicalinformation, eliminate redundant information and reduce complexity. Applyingthis model to the vehicle flow and speed data of Shenzhen and Los Angeles hasbeen well verified, and the time and memory consumption are effectively reducedunder the compared precision.

Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction