The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods.

公共地铁系统客流预测是智能交通系统的核心和关键部分，对交通管理、地铁规划和应急安全措施至关重要。大多数方法从历史数据中选择最近的路段作为输入来预测未来的交通流量；但是，这会导致地铁客流每日早晚高峰的固有特征信息丢失。因此，本研究聚合了近期和长期信息，并使用长期门控卷积神经网络（Gated CNN）从复杂的历史数据中提取时间特征。另一方面，典型模型没有考虑不同地铁站之间不同的空间依赖关系；这项工作提出了各种相邻关系来表征节点之间的关联程度。为了同时提取空间和时间特征，通过多图神经网络模块将近期和长期的历史数据合并在一起提取空间特征。通过将 Gated CNN 和多图模块相结合，我们提出了一种名为 MTMGNN 的多时间多图神经网络，用于地铁客流预测。我们在真实世界数据集上的实验结果表明，我们的模型 MTMGNN 优于所有最先进的方法。通过将 Gated CNN 和多图模块相结合，我们提出了一种名为 MTMGNN 的多时间多图神经网络，用于地铁客流预测。我们在真实世界数据集上的实验结果表明，我们的模型 MTMGNN 优于所有最先进的方法。通过将 Gated CNN 和多图模块相结合，我们提出了一种名为 MTMGNN 的多时间多图神经网络，用于地铁客流预测。我们在真实世界数据集上的实验结果表明，我们的模型 MTMGNN 优于所有最先进的方法。