Origin-destination demand prediction is a critical task in the field of intelligent transportation systems. However, accurately modeling the complex spatial-temporal dependencies presents significant challenges, which arises from various factors, including spatial, temporal, and external influences such as geographical features, weather conditions, and traffic incidents. Moreover, capturing multi-scale dependencies of local and global spatial dependencies, as well as short and long-term temporal dependencies, further complicates the task. To address these challenges, a novel framework called the Spatial-Temporal Memory Enhanced Multi-Level Attention Network (ST-MEN) is proposed. The framework consists of several key components. Firstly, an external attention mechanism is incorporated to efficiently process external factors into the prediction process. Secondly, a dynamic spatial feature extraction module is designed that effectively captures the spatial dependencies among nodes. By incorporating two skip-connections, this module preserves the original node information while aggregating information from other nodes. Finally, a temporal feature extraction module is proposed that captures both continuous and discrete temporal dependencies using a hierarchical memory network. In addition, multi-scale features cascade fusion is incorporated to enhance the performance of the proposed model. To evaluate the effectiveness of the proposed model, extensively experiments are conducted on two real-world datasets. The experimental results demonstrate that the ST-MEN model achieves excellent prediction accuracy, where the maximum improvement can reach to 19.1%.

出发地-目的地需求预测是智能交通系统领域的一项关键任务。然而，对复杂的时空依赖性进行准确建模提出了重大挑战，这些挑战来自于各种因素，包括空间、时间和地理特征、天气条件和交通事件等外部影响。此外，捕获局部和全局空间依赖性的多尺度依赖性，以及短期和长期时间依赖性，使任务进一步复杂化。为了应对这些挑战，提出了一种称为时空记忆增强多级注意力网络（ST-MEN）的新颖框架。该框架由几个关键组件组成。首先，引入外部注意力机制，将外部因素有效地处理到预测过程中。其次，设计了动态空间特征提取模块，有效捕获节点之间的空间依赖性。通过合并两个跳跃连接，该模块保留原始节点信息，同时聚合来自其他节点的信息。最后，提出了一种时间特征提取模块，该模块使用分层存储网络捕获连续和离散时间依赖性。此外，还结合了多尺度特征级联融合来增强所提出模型的性能。为了评估所提出模型的有效性，在两个真实世界数据集上进行了广泛的实验。实验结果表明，ST-MEN模型具有优异的预测精度，最大提升可达19.1%。