  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 or weekends. There are only few studies focusing on predicting
the passenger flow on holidays, which is a significantly challenging task for
traffic management because of its suddenness and irregularity. To this end, we
propose a deep learning-based model named Spatial Temporal Attention Fusion
Network comprising a novel Multi-Graph Attention Network, a Conv-Attention
Block, and Feature Fusion Block for short-term passenger flow prediction on
holidays. The multi-graph attention network is applied to extract the complex
spatial dependencies of passenger flow dynamically and the conv-attention block
is applied to extract the temporal dependencies of passenger flow from global
and local perspectives. Moreover, in addition to the historical passenger flow
data, the social media data, which has been proven that they can effectively
reflect the evolution trend of passenger flow under events, are also fused into
the feature fusion block of STAFN. The STAFN is tested on two large-scale urban
rail transit AFC datasets from China on the New Year holiday, and the
prediction performance of the model are compared with that of several
conventional prediction models. Results demonstrate its better robustness and
advantages among benchmark methods, which can provide overwhelming support for
practical applications of short term passenger flow prediction on holidays.
