This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous downstream applications such as urban planning and transportation management. However, the efficacy of existing methods remains sub-optimal due to their tendency to model temporal and spatial relationships independently, thereby inadequately accounting for complex high-order interactions of both worlds. Moreover, the diversity of transitional patterns in traffic forecasting makes them challenging to capture for existing approaches, warranting a deeper exploration of their diversity. Toward this end, this paper proposes Conjoint Spatio-Temporal graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships. On the one hand, heterogeneous graphs connecting sequential observation are constructed to extract composite spatio-temporal relationships via prior message passing. On the other hand, we model dynamic relationships using constructed affinity and penalty graphs, which guide posterior message passing to incorporate complementary semantic information into node representations. Moreover, to capture diverse transitional properties to enhance traffic forecasting, we propose a conjoint self-attention decoder that models diverse temporal patterns from both multi-rank and multi-scale views. Experimental results on four popular benchmark datasets demonstrate that our proposed COOL provides state-of-the-art performance compared with the competitive baselines.

翻译：本文研究交通预测问题，旨在基于历史状况预测未来交通状态。该问题在各类场景中受到日益关注，并推动了城市规划与交通管理等诸多下游应用的发展。然而，现有方法的效果仍非最优，原因在于其倾向于独立建模时间与空间关系，从而未能充分刻画两个维度的复杂高阶交互。此外，交通预测中转换模式的多样性使得现有方法难以捕捉，亟需对其多样性进行深入探索。为此，本文提出联合时空图神经网络（简称COOL），通过从先验与后验信息中构建异质图，联合捕捉高阶时空关系。一方面，构建连接时序观测的异质图，通过先验消息传递提取复合时空关系；另一方面，利用构建的亲和图与惩罚图对动态关系进行建模，指导后验消息传递将互补语义信息融入节点表示。此外，为捕捉多样化的转换特性以增强交通预测性能，提出联合自注意力解码器，从多秩与多尺度视角建模多样化时间模式。在四个主流基准数据集上的实验结果表明，与竞争基线相比，所提出的COOL模型实现了最先进的性能。