  Accurate passenger flow prediction of urban rail transit is essential for
improving the performance of intelligent transportation systems, especially
during the epidemic. How to dynamically model the complex spatiotemporal
dependencies of passenger flow is the main issue in achieving accurate
passenger flow prediction during the epidemic. To solve this issue, this paper
proposes a brand-new transformer-based architecture called STformer under the
encoder-decoder framework specifically for COVID-19. Concretely, we develop a
modified self-attention mechanism named Causal-Convolution ProbSparse
Self-Attention (CPSA) to model the multiple temporal dependencies of passenger
flow with low computational costs. To capture the complex and dynamic spatial
dependencies, we introduce a novel Adaptive Multi-Graph Convolution Network
(AMGCN) by leveraging multiple graphs in a self-adaptive manner. Additionally,
the Multi-source Data Fusion block fuses the passenger flow data, COVID-19
confirmed case data, and the relevant social media data to study the impact of
COVID-19 to passenger flow. Experiments on real-world passenger flow datasets
demonstrate the superiority of ST-former over the other eleven state-of-the-art
methods. Several ablation studies are carried out to verify the effectiveness
and reliability of our model structure. Results can provide critical insights
for the operation of URT systems.
