Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce the Spatio-Temporal Unitized Model (STUM), a unified framework designed to capture both spatial and temporal dependencies while addressing spatio-temporal heterogeneity through techniques such as distribution alignment and feature fusion. It also ensures both predictive accuracy and computational efficiency. Central to STUM is the Adaptive Spatio-temporal Unitized Cell (ASTUC), which utilizes low-rank matrices to seamlessly store, update, and interact with space, time, as well as their correlations. Our framework is also modular, allowing it to integrate with various spatio-temporal graph neural networks through components such as backbone models, feature extractors, residual fusion blocks, and predictive modules to collectively enhance forecasting outcomes. Experimental results across multiple real-world datasets demonstrate that STUM consistently improves prediction performance with minimal computational cost. These findings are further supported by hyperparameter optimization, pre-training analysis, and result visualization. We provide our source code for reproducibility at https://anonymous.4open.science/r/STUM-E4F0.

翻译：时空交通流预测因空间与时间因素间的复杂交互作用而面临重大挑战。现有方法通常孤立处理这两个维度，忽视了其关键的内在关联性。本文提出时空一体化模型（STUM），这是一个旨在同时捕获空间与时间依赖性，并通过分布对齐和特征融合等技术处理时空异质性的统一框架。该框架在保证预测精度的同时确保了计算效率。STUM的核心是自适应时空一体化单元（ASTUC），它利用低秩矩阵无缝地存储、更新空间、时间信息及其关联性。我们的框架还具有模块化特性，可通过骨干模型、特征提取器、残差融合块和预测模块等组件与各种时空图神经网络集成，共同提升预测效果。在多个真实世界数据集上的实验结果表明，STUM能以最小的计算成本持续提升预测性能。这些发现得到了超参数优化、预训练分析和结果可视化的进一步支持。我们已在 https://anonymous.4open.science/r/STUM-E4F0 提供源代码以确保可复现性。