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.

交通预测是智能交通系统中的一个基本问题。本文提出了一种名为 Meta Attentive Graph Convolutional Recurrent Network（MAGCRN）的新型交通预测模型，该模型能够捕捉全局节点特定模式和短期与长期依赖关系，实验证明其在短期和长期预测上均优于现有模型。

元注意图卷积循环网络用于交通预测

Meta Attentive Graph Convolutional Recurrent Network for Traffic  Forecasting

Modeling complex spatial and temporal correlations in the correlated time
series data is indispensable for understanding the traffic dynamics and
predicting the future status of an evolving traffic system. Recent works focus
on designing complicated graph neural network architectures to capture shared
patterns with the help of pre-defined graphs. In this paper, we argue that
learning node-specific patterns is essential for traffic forecasting while the
pre-defined graph is avoidable. To this end, we propose two adaptive modules
for enhancing Graph Convolutional Network (GCN) with new capabilities: 1) a
Node Adaptive Parameter Learning (NAPL) module to capture node-specific
patterns; 2) a Data Adaptive Graph Generation (DAGG) module to infer the
inter-dependencies among different traffic series automatically. We further
propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture
fine-grained spatial and temporal correlations in traffic series automatically
based on the two modules and recurrent networks. Our experiments on two
real-world traffic datasets show AGCRN outperforms state-of-the-art by a
significant margin without pre-defined graphs about spatial connections.

本文提出自适应图卷积循环神经网络 AGCRN，其中包括两个自适应模块（节点自适应参数学习模块和数据自适应图生成模块），用于捕捉交通时间序列数据中的空间和时间相关性。实验结果表明，AGCRN 在不需要预先定义空间连接图的情况下显著优于现有的相关方法。