基于物理引导深度学习的突发公共卫生事件期间城市轨道交通短时OD客流预测

章树鑫; 张金雷; 杨立兴; 陈峰; 李树凯; 高自友

doi:10.1016/j.eng.2024.04.020

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工程（英文） ›› 2024, Vol. 41 ›› Issue (10) : 276-296. DOI: 10.1016/j.eng.2024.04.020

研究论文

Article

基于物理引导深度学习的突发公共卫生事件期间城市轨道交通短时OD客流预测

章树鑫 ^a ,
张金雷 ^a ,
杨立兴 ^a ,
陈峰 ^b ,
李树凯 ^a ,
高自友 ^a

作者信息 +

Physics Guided Deep Learning-Based Model for Short-Term Origin-Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Shuxin Zhang ^a ,
Jinlei Zhang ^a^,^* ,
Lixing Yang ^a ,
Feng Chen ^b ,
Shukai Li ^a ,
Ziyou Gao ^a

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Abstract

Accurate origin-destination (OD) demand prediction is crucial for the efficient operation and management of urban rail transit (URT) systems, particularly during a pandemic. However, this task faces several limitations, including real-time availability, sparsity, and high-dimensionality issues, and the impact of the pandemic. Consequently, this study proposes a unified framework called the physics-guided adaptive graph spatial-temporal attention network (PAG-STAN) for metro OD demand prediction under pandemic conditions. Specifically, PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices. Subsequently, a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices. Thereafter, PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic. Finally, a masked physics-guided loss function (MPG-loss function) incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability. PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios, highlighting its robustness and sensitivity for metro OD demand prediction. A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Keywords

Short-term origin-destination demand prediction / Urban rail transit / Pandemic

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章树鑫, 张金雷, 杨立兴. 基于物理引导深度学习的突发公共卫生事件期间城市轨道交通短时OD客流预测. Engineering. 2024, 41(10): 276-296 https://doi.org/10.1016/j.eng.2024.04.020

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