  Urban metro flow prediction is of great value for metro operation scheduling,
passenger flow management and personal travel planning. However, it faces two
main challenges. First, different metro stations, e.g. transfer stations and
non-transfer stations, have unique traffic patterns. Second, it is challenging
to model complex spatio-temporal dynamic relation of metro stations. To address
these challenges, we develop a spatio-temporal dynamic graph relational
learning model (STDGRL) to predict urban metro station flow. First, we propose
a spatio-temporal node embedding representation module to capture the traffic
patterns of different stations. Second, we employ a dynamic graph relationship
learning module to learn dynamic spatial relationships between metro stations
without a predefined graph adjacency matrix. Finally, we provide a
transformer-based long-term relationship prediction module for long-term metro
flow prediction. Extensive experiments are conducted based on metro data in
Beijing, Shanghai, Chongqing and Hangzhou. Experimental results show the
advantages of our method beyond 11 baselines for urban metro flow prediction.
