  This study proposes a novel Graph Convolutional Neural Network with
Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous
pairwise correlations among stations to predict station-level hourly demand in
a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model
are explored: GCNNreg-DDGF is a regular GCNN-DDGF model which contains the
convolution and feedforward blocks; GCNNrec-DDGF additionally contains a
recurrent block from the Long Short-term Memory neural network to capture
temporal dependencies in bike-sharing demand series. Furthermore, four GCNN
models are proposed whose adjacency matrices are based on various bike-sharing
system data, including Spatial Distance matrix (SD), Demand matrix (DE),
Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These
six GCNN models along with seven other benchmark models are built and compared
using the Citi Bike dataset from New York City, which includes 272 stations and
over 28 million transactions from 2013 to 2016. Results show that the
GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean
Absolute Error, and the coefficient of determination (R2), followed by the
GCNNreg-DDGF. They outperform the other models.
