Traffic forecasting is a fundamental problem in intelligent transportation systems. Existing traffic predictors are limited by their expressive power to model the complex spatial-temporal dependencies in traffic data, mainly due to the following limitations. Firstly, most approaches are primarily designed to model the local shared patterns, which makes them insufficient to capture the specific patterns associated with each node globally. Hence, they fail to learn each node's unique properties and diversified patterns. Secondly, most existing approaches struggle to accurately model both short- and long-term dependencies simultaneously. In this paper, we propose a novel traffic predictor, named Meta Attentive Graph Convolutional Recurrent Network (MAGCRN). MAGCRN utilizes a Graph Convolutional Recurrent Network (GCRN) as a core module to model local dependencies and improves its operation with two novel modules: 1) a Node-Specific Meta Pattern Learning (NMPL) module to capture node-specific patterns globally and 2) a Node Attention Weight Generation Module (NAWG) module to capture short- and long-term dependencies by connecting the node-specific features with the ones learned initially at each time step during GCRN operation. Experiments on six real-world traffic datasets demonstrate that NMPL and NAWG together enable MAGCRN to outperform state-of-the-art baselines on both short- and long-term predictions.

翻译：交通预测是智能交通系统中的基础问题。现有交通预测器在建模交通数据中复杂的时空依赖关系时受限于其表达能力，主要存在以下不足。首先，大多数方法主要用于建模局部共享模式，导致其难以捕捉每个节点全局范围内的特定模式，从而无法学习各节点的独特属性与多样化模式。其次，多数现有方法难以同时准确建模短期与长期依赖关系。本文提出一种名为元注意力图卷积循环网络（MAGCRN）的新型交通预测器。MAGCRN以图卷积循环网络（GCRN）为核心模块建模局部依赖关系，并通过两个创新模块增强其性能：1）节点特定元模式学习模块（NMPL），用于全局捕捉节点特定模式；2）节点注意力权重生成模块（NAWG），通过在GCRN运行过程中将节点特定特征与每个时间步初始学习的特征相连，从而捕捉短期与长期依赖关系。在六个真实交通数据集上的实验表明，NMPL与NAWG共同使MAGCRN在短期与长期预测任务上均优于当前最先进的基线方法。