  Short-term passenger flow forecasting is an essential component in urban rail
transit operation. Emerging deep learning models provide good insight into
improving prediction precision. Therefore, we propose a deep learning
architecture combining the residual network (ResNet), graph convolutional
network (GCN), and long short-term memory (LSTM) (called "ResLSTM") to forecast
short-term passenger flow in urban rail transit on a network scale. First,
improved methodologies of the ResNet, GCN, and attention LSTM models are
presented. Then, the model architecture is proposed, wherein ResNet is used to
capture deep abstract spatial correlations between subway stations, GCN is
applied to extract network topology information, and attention LSTM is used to
extract temporal correlations. The model architecture includes four branches
for inflow, outflow, graph-network topology, as well as weather conditions and
air quality. To the best of our knowledge, this is the first time that
air-quality indicators have been taken into account, and their influences on
prediction precision quantified. Finally, ResLSTM is applied to the Beijing
subway using three time granularities (10, 15, and 30 min) to conduct
short-term passenger flow forecasting. A comparison of the prediction
performance of ResLSTM with those of many state-of-the-art models illustrates
the advantages and robustness of ResLSTM. Moreover, a comparison of the
prediction precisions obtained for time granularities of 10, 15, and 30 min
indicates that prediction precision increases with increasing time granularity.
This study can provide subway operators with insight into short-term passenger
flow forecasting by leveraging deep learning models.
