交通预测是智能交通系统与城市尺度决策的核心任务。尽管主流基于神经网络的方法效果显著，但其在部署于拥有数千个交通传感器的真实场景时，因计算可扩展性差而严重受限。为解决此问题，研究社区尝试引入空间数据库划分技术以提升模型可扩展性。然而，这些方法依赖人工设计的几何启发式策略，常导致不规则或不平衡的数据划分，进而引发边界碎片化、填充开销过大及模型精度下降等问题。本文提出 SqLinear，一种面向大规模交通预测的高效且有效的架构。首先，我们设计了方形划分（Square Partition），一种几何自适应算法，将海量交通传感器划分为均衡、互不重叠且空间紧凑的区域。与现有基于启发式的设计不同，方形划分具有理论基础，可对划分利用率与分割平衡性提供可证明的保障，从而为下游时空建模奠定高质量基础。其次，我们提出分层线性交互（Hierarchical Linear Interaction, HLI）模块，摒弃了 Transformer 类时空模型中常见的高成本注意力机制。HLI 通过轻量级线性交互方案高效传播全局区域间依赖关系，并在节点层级对其进行细化，从而以线性计算复杂度实现有效的时空建模。在四个大规模交通数据集及 11 种基线方法上的大量实验表明，SqLinear 在标准设置下平均降低 MAE 2.30%，在极端可扩展性设置下最高降低 MAE 6.78%，同时减少……

Traffic prediction is a core task in intelligent transportation systems and urban-scale decision making. Despite the effectiveness of mainstream neural network-based methods, their deployment in real-world settings with thousands of traffic sensors is severely jeopardized by their poor computational scalability. To address this, the community has attempted to incorporate spatial database partitioning techniques to improve model scalability. However, these approaches rely on handcrafted geometric heuristics and often produce irregular or imbalanced data partitions, leading to boundary fragmentation, excessive padding overheads, and degraded model accuracy. In this paper, we propose SqLinear, an efficient and effective architecture for large-scale traffic prediction. First, we design Square Partition, a geometry-adaptive algorithm that partitions massive traffic sensors into balanced, non-overlapping, and compact spatial regions. Unlike existing heuristic-based designs, Square Partition is theoretically grounded and provides provable guarantees on partition utilization and split balance, establishing a high-quality foundation for downstream spatio-temporal modeling. Next, we propose a Hierarchical Linear Interaction (HLI) module that abandons the costly attention mechanisms commonly used in Transformer-based spatio-temporal models. HLI efficiently propagates global inter-region dependencies and refines them at the node level through a lightweight linear interaction scheme, enabling effective spatio-temporal modeling with linear computational complexity. Extensive experiments on four large-scale traffic datasets and 11 baselines show that SqLinear reduces MAE by 2.30% on average under the standard setting and by up to 6.78% under extreme scalability settings, while reducing training runtime by 13.27%--30.84% in spatial- and horizon-scaling scenarios.