  Accurate traffic conditions prediction provides a solid foundation for
vehicle-environment coordination and traffic control tasks. Because of the
complexity of road network data in spatial distribution and the diversity of
deep learning methods, it becomes challenging to effectively define traffic
data and adequately capture the complex spatial nonlinear features in the data.
This paper applies two hierarchical graph pooling approaches to the traffic
prediction task to reduce graph information redundancy. First, this paper
verifies the effectiveness of hierarchical graph pooling methods in traffic
prediction tasks. The hierarchical graph pooling methods are contrasted with
the other baselines on predictive performance. Second, two mainstream
hierarchical graph pooling methods, node clustering pooling and node drop
pooling, are applied to analyze advantages and weaknesses in traffic
prediction. Finally, for the mentioned graph neural networks, this paper
compares the predictive effects of different graph network inputs on traffic
prediction accuracy. The efficient ways of defining graph networks are analyzed
and summarized.
