  Due to the widespread applications in real-world scenarios, metro ridership
prediction is a crucial but challenging task in intelligent transportation
systems. However, conventional methods either ignore the topological
information of metro systems or directly learn on physical topology, and cannot
fully explore the patterns of ridership evolution. To address this problem, we
model a metro system as graphs with various topologies and propose a unified
Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively
learn the complex ridership patterns from the tailor-designed graphs.
Specifically, a physical graph is directly built based on the realistic
topology of the studied metro system, while a similarity graph and a
correlation graph are built with virtual topologies under the guidance of the
inter-station passenger flow similarity and correlation. These complementary
graphs are incorporated into a Graph Convolution Gated Recurrent Unit (GC-GRU)
for spatial-temporal representation learning. Further, a Fully-Connected Gated
Recurrent Unit (FC-GRU) is also applied to capture the global evolution
tendency. Finally, we develop a Seq2Seq model with GC-GRU and FC-GRU to
forecast the future metro ridership sequentially. Extensive experiments on two
large-scale benchmarks (e.g., Shanghai Metro and Hangzhou Metro) well
demonstrate the superiority of our PVCGN for station-level metro ridership
prediction. Moreover, we apply the proposed PVCGN to address the online
origin-destination (OD) ridership prediction and the experiment results show
the universality of our method. Our code and benchmarks are available at
https://github.com/HCPLab-SYSU/PVCGN.
