Spatial–temporal data is widely available in intelligent transportation systems, and accurately solving non-stationary of spatial–temporal regression is critical. In most traffic flow prediction research, the non-stationary solution of deep spatial–temporal regression tasks is typically formulated as a spatial–temporal graph modeling problem. However, there are several issues: (1) the coupled spatial–temporal regression approach renders it unfeasible to accurately learn the dependencies of diverse modalities; (2) the intricate stacking design of deep spatial–temporal network modules limits the interpretation and migration capability; (3) the ability to model dynamic spatial–temporal relationships is inadequate. To tackle the challenges mentioned above, we propose a novel unified spatial–temporal regression framework named Generalized Spatial–Temporal Regression Graph Convolutional Transformer (GSTRGCT) that extends panel model in spatial econometrics and combines it with deep neural networks to effectively model non-stationary relationships of spatial–temporal regression. Considering the coupling of existing deep spatial–temporal networks, we introduce the tensor decomposition to explicitly decompose the panel model into a tensor product of spatial regression on the spatial hyper-plane and temporal regression on the temporal hyper-plane. On the spatial hyper-plane, we present dynamic adaptive spatial weight network (DASWNN) to capture the global and local spatial correlations. Specifically, DASWNN adopts spatial weight neural network (SWNN) to learn the semantic global spatial correlation and dynamically adjusts the local changing spatial correlation by multiplying between spatial nodes embedding. On the temporal hyper-plane, we introduce the Auto-Correlation attention mechanism to capture the period-based temporal dependence. Extensive experiments on the two real-world traffic datasets show that GSTRGCT consistently outperforms other competitive methods with an average of 62% and 59% on predictive performance.

时空数据在智能交通系统中广泛存在，准确求解时空回归的非平稳性至关重要。在大多数交通流预测研究中，深度时空回归任务的非平稳解通常被表述为时空图建模问题。然而，存在几个问题：（1）时空耦合回归方法使得准确学习不同模态的依赖关系变得不可行； （2）深层时空网络模块错综复杂的堆叠设计限制了解释和迁移能力； (3)动态时空关系建模能力不足。为了应对上述挑战，我们提出了一种新颖的统一时空回归框架，名为广义时空回归图卷积变换器（GSTRGCT），该框架扩展了空间计量经济学中的面板模型，并将其与深度神经网络相结合，以有效地建模非平稳关系时空回归。考虑到现有深层时空网络的耦合，我们引入张量分解，将面板模型显式分解为空间超平面上的空间回归和时间超平面上的时间回归的张量积。在空间超平面上，我们提出动态自适应空间权重网络（DASWNN）来捕获全局和局部空间相关性。具体来说，DASWNN采用空间权重神经网络（SWNN）来学习语义全局空间相关性，并通过空间节点嵌入之间的相乘来动态调整局部变化的空间相关性。 在时间超平面上，我们引入自相关注意机制来捕获基于周期的时间依赖性。对两个真实世界流量数据集的大量实验表明，GSTRGCT 的预测性能始终优于其他竞争方法，平均分别为 62% 和 59%。