  Short-term passenger flow forecasting is a crucial task for urban rail
transit operations. Emerging deep-learning technologies have become effective
methods used to overcome this problem. In this study, the authors propose a
deep-learning architecture called Conv-GCN that combines a graph convolutional
network (GCN) and a three-dimensional (3D) convolutional neural network (3D
CNN). First, they introduce a multi-graph GCN to deal with three inflow and
outflow patterns (recent, daily, and weekly) separately. Multi-graph GCN
networks can capture spatiotemporal correlations and topological information
within the entire network. A 3D CNN is then applied to deeply integrate the
inflow and outflow information. High-level spatiotemporal features between
different inflow and outflow patterns and between stations that are nearby and
far away can be extracted by 3D CNN. Finally, a fully connected layer is used
to output results. The Conv-GCN model is evaluated on smart card data of the
Beijing subway under the time interval of 10, 15, and 30 min. Results show that
this model yields the best performance compared with seven other models. In
terms of the root-mean-square errors, the performances under three time
intervals have been improved by 9.402, 7.756, and 9.256%, respectively. This
study can provide critical insights for subway operators to optimise urban rail
transit operations.
