  Traffic forecasting is one of the most fundamental problems in transportation
science and artificial intelligence. The key challenge is to effectively model
complex spatial-temporal dependencies and correlations in modern traffic data.
Existing methods, however, cannot accurately model both long-term and
short-term temporal correlations simultaneously, limiting their expressive
power on complex spatial-temporal patterns. In this paper, we propose a novel
spatial-temporal neural network framework: Attention-based Spatial-Temporal
Graph Convolutional Recurrent Network (ASTGCRN), which consists of a graph
convolutional recurrent module (GCRN) and a global attention module. In
particular, GCRN integrates gated recurrent units and adaptive graph
convolutional networks for dynamically learning graph structures and capturing
spatial dependencies and local temporal relationships. To effectively extract
global temporal dependencies, we design a temporal attention layer and
implement it as three independent modules based on multi-head self-attention,
transformer, and informer respectively. Extensive experiments on five real
traffic datasets have demonstrated the excellent predictive performance of all
our three models with all their average MAE, RMSE and MAPE across the test
datasets lower than the baseline methods.
