  Graph Convolutional Network (GCN) has been widely applied in transportation
demand prediction due to its excellent ability to capture non-Euclidean spatial
dependence among station-level or regional transportation demands. However, in
most of the existing research, the graph convolution was implemented on a
heuristically generated adjacency matrix, which could neither reflect the real
spatial relationships of stations accurately, nor capture the multi-level
spatial dependence of demands adaptively. To cope with the above problems, this
paper provides a novel graph convolutional network for transportation demand
prediction. Firstly, a novel graph convolution architecture is proposed, which
has different adjacency matrices in different layers and all the adjacency
matrices are self-learned during the training process. Secondly, a layer-wise
coupling mechanism is provided, which associates the upper-level adjacency
matrix with the lower-level one. It also reduces the scale of parameters in our
model. Lastly, a unitary network is constructed to give the final prediction
result by integrating the hidden spatial states with gated recurrent unit,
which could capture the multi-level spatial dependence and temporal dynamics
simultaneously. Experiments have been conducted on two real-world datasets, NYC
Citi Bike and NYC Taxi, and the results demonstrate the superiority of our
model over the state-of-the-art ones.
