  Air pollution and carbon emissions caused by modern transportation are
closely related to global climate change. With the help of next-generation
information technology such as Internet of Things (IoT) and Artificial
Intelligence (AI), accurate traffic flow prediction can effectively solve
problems such as traffic congestion and mitigate environmental pollution and
climate change. It further promotes the development of Intelligent
Transportation Systems (ITS) and smart cities. However, the strong spatial and
temporal correlation of traffic data makes the task of accurate traffic
forecasting a significant challenge. Existing methods are usually based on
graph neural networks using predefined spatial adjacency graphs of traffic
networks to model spatial dependencies, ignoring the dynamic correlation of
relationships between road nodes. In addition, they usually use independent
Spatio-temporal components to capture Spatio-temporal dependencies and do not
effectively model global Spatio-temporal dependencies. This paper proposes a
new Spatio-temporal Causal Graph Attention Network (STCGAT) for traffic
prediction to address the above challenges. In STCGAT, we use a node embedding
approach that can adaptively generate spatial adjacency subgraphs at each time
step without a priori geographic knowledge and fine-grained modeling of the
topology of dynamically generated graphs for different time steps. Meanwhile,
we propose an efficient causal temporal correlation component that contains
node adaptive learning, graph convolution, and local and global causal temporal
convolution modules to learn local and global Spatio-temporal dependencies
jointly. Extensive experiments on four real, large traffic datasets show that
our model consistently outperforms all baseline models.

Machine Learning;Artificial Intelligence