Traffic flow prediction is a typical spatio-temporal prediction problem and has a wide range of applications. The core challenge lies in modeling the underlying complex spatio-temporal dependencies. Various methods have been proposed, and recent studies show that the modeling of dynamics is useful to meet the core challenge. While handling spatial dependencies and temporal dependencies using separate base model structures may hinder the modeling of spatio-temporal correlations, the modeling of dynamics can bridge this gap. Incorporating spatio-temporal heterogeneity also advances the main goal, since it can extend the parameter space and allow more flexibility. Despite these advances, two limitations persist: 1) the modeling of dynamics is often limited to the dynamics of spatial topology (e.g., adjacency matrix changes), which, however, can be extended to a broader scope; 2) the modeling of heterogeneity is often separated for spatial and temporal dimensions, but this gap can also be bridged by the modeling of dynamics. To address the above limitations, we propose a novel framework for traffic prediction, called Meta Dynamic Graph (MetaDG). MetaDG leverages dynamic graph structures of node representations to explicitly model spatio-temporal dynamics. This generates both dynamic adjacency matrices and meta-parameters, extending dynamic modeling beyond topology while unifying the capture of spatio-temporal heterogeneity into a single dimension. Extensive experiments on four real-world datasets validate the effectiveness of MetaDG.

翻译：交通流量预测是一个典型的时空预测问题，具有广泛的应用前景。其核心挑战在于对潜在的复杂时空依赖关系进行建模。已有多种方法被提出，近期研究表明动态建模有助于应对这一核心挑战。虽然使用分离的基础模型结构处理空间依赖性和时间依赖性可能阻碍时空相关性的建模，但动态建模能够弥合这一差距。融入时空异质性同样能推进主要目标，因为它可以扩展参数空间并允许更大的灵活性。尽管取得了这些进展，仍存在两个局限性：1）动态建模通常局限于空间拓扑的动态性（例如邻接矩阵的变化），然而其可扩展至更广的范围；2）异质性建模通常在空间和时间维度上分离，但这一差距同样可通过动态建模来弥合。为应对上述局限，我们提出了一种名为元动态图（MetaDG）的新型交通预测框架。MetaDG利用节点表征的动态图结构来显式建模时空动态性。这既生成了动态邻接矩阵，也产生了元参数，从而将动态建模扩展到拓扑之外，同时将时空异质性的捕捉统一至单一维度。在四个真实数据集上的大量实验验证了MetaDG的有效性。