论文标题
在城市铁路运输系统中，在COVID-19期间的短期客运流程预测的ST形式
ST-former for short-term passenger flow prediction during COVID-19 in urban rail transit system
论文作者
论文摘要
城市铁路运输的准确乘客流量预测对于改善智能运输系统的性能至关重要，尤其是在流行病期间。如何动态建模乘客流的复杂时空依赖性是在流行期间实现准确的乘客流量预测的主要问题。为了解决此问题，本文提出了一个基于全新的变压器的架构，称为stformer，在编码器编码器框架下专门针对COVID-19。具体而言，我们开发了一种称为因果卷积概率自我注意力（CPSA）的修改的自我注意事项机制，以对乘客流量的多个时间依赖性建模，计算成本较低。为了捕获复杂而动态的空间依赖性，我们通过以自适应方式利用多个图形来引入一种新型的自适应多浪潮卷积网络（AMGCN）。此外，多源数据融合块融合了乘客流量数据，Covid-19确认了案例数据，以及相关的社交媒体数据，以研究Covid-19对乘客流量的影响。现实世界中客流数据集的实验证明了STFormer的优越性，而不是其他11种最先进的方法。进行了几项消融研究，以验证我们的模型结构的有效性和可靠性。结果可以为URT系统的运行提供关键见解。
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.
