The short-term passenger flow prediction of the urban rail transit system is
of great significance for traffic operation and management. The emerging deep
learning-based models provide effective methods to improve prediction accuracy.
However, most of the existing models mainly predict the passenger flow on
general weekdays, while few studies focus on predicting the holiday passenger
flow, which can provide more significant information for operators because
congestions or accidents generally occur on holidays. To this end, we propose a
deep learning-based model named GCN-Transformer comprising graph conventional
neural network (GCN) and Transformer for short-term passenger flow prediction
on holidays. The GCN is applied to extract the spatial features of passenger
flows and the Transformer is applied to extract the temporal features of
passenger flows. Moreover, in addition to the historical passenger flow data,
social media data are also incorporated into the prediction model, which has
been proven to have a potential correlation with the fluctuation of passenger
flow. The GCN-Transformer is tested on two large-scale real-world datasets from
Nanning, China during the New Year holiday and is compared with several
conventional prediction models. Results demonstrate its better robustness and
advantages among baseline methods, which provides overwhelming support for
practical applications of short-term passenger flow prediction on holidaysComment: 26 pages, 10 figures, 5 table