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.

中文翻译：

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基于物理引导的深度学习模型用于疫情下城市轨道交通系统短期起点-目的地需求预测


准确的起点-目的地 （OD） 需求预测对于城市轨道交通 （URT） 系统的高效运营和管理至关重要，尤其是在疫情期间。但是，此任务面临一些限制，包括实时可用性、稀疏性和高维性问题，以及大流行的影响。因此，本研究提出了一个统一的框架，称为物理引导的自适应图时空注意力网络 （PAG-STAN），用于大流行条件下的地铁 OD 需求预测。具体来说，PAG-STAN 引入了一个实时 OD 估计模块，用于估计实时完整的 OD 需求矩阵。随后，提出了一种新的动态 OD 需求矩阵压缩模块来生成密集的实时 OD 需求矩阵。此后，PAG-STAN 利用各种异构数据来了解大流行期间未来 OD 乘客量的演变趋势。最后，掩蔽物理导向损失函数 （MPG-loss function） 将 OD 需求和入站流量之间的物理量信息合并到损失函数中，以增强模型的可解释性。PAG-STAN 在大流行和常规情景下在两个真实世界的地铁 OD 需求数据集上表现出良好的性能，凸显了其对地铁 OD 需求预测的稳健性和敏感性。进行了一系列消融研究，以验证 PAG-STAN 中每个模块的不可或缺性。