Real-time and precise traffic flow prediction is vital for the efficiency of intelligent transportation systems. Traditional methods often employ graph neural networks (GNNs) with predefined graphs to describe spatial correlations among traffic nodes in urban road networks. However, these pre-defined graphs are limited by existing knowledge and graph generation methodologies, offering an incomplete picture of spatial correlations. While time-varying graphs based on data-driven learning have attempted to address these limitations, they still struggle with adequately capturing the inherent spatial correlations in traffic data. Moreover, most current methods for capturing dynamic temporal correlations rely on a unified calculation scheme using a temporal multi-head self-attention mechanism, which at some level might leads to inaccuracies. In order to overcome these challenges, we have proposed a novel hybrid time-varying graph neural network (HTVGNN) for traffic flow prediction. Firstly, a novel enhanced temporal perception multi-head self-attention mechanism based on time-varying mask enhancement was reported to more accurately model the dynamic temporal dependencies among distinct traffic nodes in the traffic network. Secondly, we have proposed a novel graph learning strategy to concurrently learn both static and dynamic spatial associations between different traffic nodes in road networks. Meanwhile, in order to enhance the learning ability of time-varying graphs, a coupled graph learning mechanism was designed to couple the graphs learned at each time step. Finally, the effectiveness of the proposed method HTVGNN was demonstrated with four real data sets. Simulation results revealed that HTVGNN achieves superior prediction accuracy compared to the state of the art spatio-temporal graph neural network models. Additionally, the ablation experiment verifies that the coupled graph learning mechanism can effectively improve the long-term prediction performance of HTVGNN.

翻译：实时且精确的交通流预测对于智能交通系统的效率至关重要。传统方法通常采用具有预定义图的图神经网络（GNNs）来描述城市道路网络中交通节点间的空间相关性。然而，这些预定义图受限于现有知识和图生成方法，无法完整地刻画空间相关性。虽然基于数据驱动学习的时变图已尝试解决这些局限性，但它们仍难以充分捕捉交通数据中固有的空间相关性。此外，当前大多数捕捉动态时间相关性的方法依赖于使用时态多头自注意力机制的统一计算方案，这在某种程度上可能导致不准确性。为了克服这些挑战，我们提出了一种用于交通流预测的新型混合时变图神经网络（HTVGNN）。首先，我们提出了一种基于时变掩码增强的新型增强时态感知多头自注意力机制，以更精确地建模交通网络中不同交通节点间的动态时间依赖性。其次，我们提出了一种新颖的图学习策略，以同时学习道路网络中不同交通节点间的静态和动态空间关联。同时，为了增强时变图的学习能力，我们设计了一种耦合图学习机制，以耦合在每个时间步学习到的图。最后，我们使用四个真实数据集验证了所提方法HTVGNN的有效性。仿真结果表明，与最先进的时空图神经网络模型相比，HTVGNN实现了更优的预测精度。此外，消融实验验证了耦合图学习机制能够有效提升HTVGNN的长期预测性能。