  The prediction of urban vehicle flow and speed can greatly facilitate
people's travel, and also can provide reasonable advice for the decision-making
of relevant government departments. However, due to the spatial, temporal and
hierarchy of vehicle flow and many influencing factors such as weather, it is
difficult to prediction. Most of the existing research methods are to extract
spatial structure information on the road network and extract time series
information from the historical data. However, when extracting spatial
features, these methods have higher time and space complexity, and incorporate
a lot of noise. It is difficult to apply on large graphs, and only considers
the influence of surrounding connected road nodes on the central node, ignoring
a very important hierarchical relationship, namely, similar information of
similar node features and road network structures. In response to these
problems, this paper proposes the Graph Hierarchical Convolutional Recurrent
Neural Network (GHCRNN) model. The model uses GCN (Graph Convolutional
Networks) to extract spatial feature, GRU (Gated Recurrent Units) to extract
temporal feature, and uses the learnable Pooling to extract hierarchical
information, eliminate redundant information and reduce complexity. Applying
this model to the vehicle flow and speed data of Shenzhen and Los Angeles has
been well verified, and the time and memory consumption are effectively reduced
under the compared precision.
