Meta Attentive Graph Convolutional Recurrent Network for Traffic
  Forecasting
Adnan Zeb Yongchao Ye Shiyao Zhang James J. Q. Yu
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.