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.

翻译：准确的交通条件预测为车辆-环境协调和交通控制任务提供了坚实的基础。由于公路网络数据在空间分布方面的复杂性和深层学习方法的多样性,有效界定交通数据并充分捕捉数据中复杂的空间非线性特征变得很困难。本文对交通预测任务采用了两种等级图形汇总方法,以减少图形信息冗余。首先,本文件核实了运输预测任务中等级图形汇总方法的有效性。等级图形汇总方法与预测性能的其他基线进行了对比。第二,运用了两种主流等级图形集合方法,即节聚和节滴集合,以分析交通预测的优缺点。最后,对于上述图表神经网络,本文比较了不同图形网络投入对交通预测准确性的预测效果。分析和总结了界定图形网络的有效方法。