The spatial–temporal graph neural networks have been a critical approach to capturing the complicated spatial–temporal dependencies inherent in traffic series for more accurate forecasting. However, the issue of graph indistinguishability demands further attention, as graphs learned by existing methods tend to converge to implicit and indistinguishable representations, deviating from the genuine distribution. This issue can be attributed to the lack of three primary factors within graphs: the intrinsic graph features, the temporal-distinct features, and the node-distinct features. Aiming to address this problem, we propose a emporal-ware tructure-emantic-Coupled raph etwork (TASSGN) in this paper. Firstly, we design a novel graph learning block to simultaneously learn the structural and semantic aspects of graphs, thereby capturing inherent graph features. Secondly, we propose an innovative Self-Sampling method to sample the relevant history series and present a Temporal-Aware Graphs Encoder to explicitly incorporate temporal information into graph learning and capture temporal-distinct features. Thirdly, sparse graphs are intentionally generated to capture node-distinct features. By combining these three key components together, our method is capable of overcoming the problem of graph indistinguishability and achieving state-of-the-art performances in traffic forecasting.

中文翻译：

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用于流量预测的时间感知结构语义耦合图网络

时空图神经网络是捕获交通序列中固有的复杂时空依赖性以进行更准确预测的关键方法。然而，图不可区分性问题需要进一步关注，因为现有方法学习的图往往会收敛到隐式且不可区分的表示，偏离真实分布。这个问题可以归因于图中缺乏三个主要因素：内在图特征、时间不同特征和节点不同特征。为了解决这个问题，我们在本文中提出了一种 emporal-ware structure-emantic-Coupled raph etwork (TASSGN)。首先，我们设计了一种新颖的图学习模块来同时学习图的结构和语义方面，从而捕获固有的图特征。其次，我们提出了一种创新的自采样方法来对相关历史序列进行采样，并提出了一种时间感知图编码器，以将时间信息明确地纳入图学习中并捕获时间不同的特征。第三，有意生成稀疏图来捕获节点不同的特征。通过将这三个关键组件结合在一起，我们的方法能够克服图不可区分性的问题，并在流量预测中实现最先进的性能。