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.

翻译：准确预测城市轨道交通客流量对于提升智能交通系统性能至关重要，尤其是在疫情期间。如何动态建模客流量复杂的时空依赖关系是实现疫情期间精准客流预测的关键问题。为解决该问题，本文提出一种全新的基于Transformer的架构——STformer，该架构采用编码器-解码器框架，专门针对COVID-19场景设计。具体而言，我们开发了一种改进的自注意力机制——因果卷积ProbSparse自注意力（Causal-Convolution ProbSparse Self-Attention, CPSA），以较低的算力成本建模客流量多重时间依赖关系。为捕捉复杂动态的空间依赖关系，我们引入了一种新型自适应多图卷积网络（Adaptive Multi-Graph Convolution Network, AMGCN），以自适应方式利用多张图结构。此外，多源数据融合模块融合了客流量数据、COVID-19确诊病例数据及相关社交媒体数据，以研究疫情对客流量的影响。在实际客流量数据集上的实验表明，ST-former在性能上优于其他十一种最先进方法。通过多项消融研究验证了我们模型结构的有效性和可靠性。研究结果为城市轨道交通系统的运营提供了重要启示。