Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as signal control and network-level traffic management. In real-world deployments, forecasting models must operate under structural and observational uncertainties, conditions that are rarely considered in model design. Recent approaches achieve strong short-term predictive performance by tightly coupling spatial and temporal modeling, often at the cost of increased complexity and limited modularity. In contrast, efficient time-series models capture long-range temporal dependencies without relying on explicit network structure. We propose UniST-Pred, a unified spatio-temporal forecasting framework that first decouples temporal modeling from spatial representation learning, then integrates both through adaptive representation-level fusion. To assess robustness of the proposed approach, we construct a dataset based on an agent-based, microscopic traffic simulator (MATSim) and evaluate UniST-Pred under severe network disconnection scenarios. Additionally, we benchmark UniST-Pred on standard traffic prediction datasets, demonstrating its competitive performance against existing well-established models despite a lightweight design. The results illustrate that UniST-Pred maintains strong predictive performance across both real-world and simulated datasets, while also yielding interpretable spatio-temporal representations under infrastructure disruptions. The source code and the generated dataset are available at https://anonymous.4open.science/r/UniST-Pred-EF27

翻译：时空交通预测是智能交通系统的核心组成部分，为信号控制和网络级交通管理等各类下游任务提供支持。在实际部署中，预测模型必须在结构性和观测性不确定条件下运行，而这些条件在模型设计中鲜少被考虑。现有方法通过紧密耦合时空建模实现了强大的短期预测性能，但往往以增加复杂度和牺牲模块化为代价。相比之下，高效的时间序列模型能够在不依赖显式网络结构的情况下捕获长程时间依赖性。本文提出UniST-Pred，一种统一的时空预测框架，该框架首先将时序建模与空间表征学习解耦，随后通过自适应表征级融合实现两者的集成。为评估所提方法的鲁棒性，我们基于基于智能体的微观交通仿真器（MATSim）构建数据集，并在严重网络中断场景下对UniST-Pred进行评估。此外，我们在标准交通预测数据集上对UniST-Pred进行基准测试，证明其轻量化设计下仍能取得与现有成熟模型相竞争的预测性能。实验结果表明，UniST-Pred在真实数据集和仿真数据集上均保持强大的预测性能，同时能在基础设施中断条件下生成可解释的时空表征。源代码与生成的数据集已发布于 https://anonymous.4open.science/r/UniST-Pred-EF27