交通预测对智慧城市中的高效交通管理和路径规划至关重要。现有交通预测研究通常假设传感器图结构固定，忽视了现实世界交通网络的持续演化，例如道路网络的持续建设与人类移动模式的动态变化。此类动态变化会显著降低传统预测模型的性能，从而促使研究者采用测试时自适应（Test-Time Adaptation, TTA）方法，在部署阶段高效调整预训练模型。然而，将TTA应用于演进型交通传感器网络仍面临两大挑战：其一，拓扑扩展引入新传感器及连接关系，持续重塑传感器图；其二，时间偏移在时间尺度与稳定性上存在差异，需对长期与短期偏移实施差异化自适应。本研究提出A2TTA——一种面向演进型交通传感器网络的锚定-敏捷测试时自适应框架，该框架将拓扑演化引发的预测误差转化为可扩展的输出校准问题，并将时间自适应解耦为持久性全局校正与敏捷的上下文特异性专业化。通过协同应对拓扑演化与多尺度时间偏移，A2TTA实现了对持续演化的交通环境的高效且鲁棒的自适应。在十个真实交通网络上的大量实验表明，A2TTA在不同骨干网络、数据集及预测时域下均能持续提升预测性能。代码开源地址：https://github.com/lixus7/A2TTA。

Traffic forecasting is important for efficient traffic management and route planning in smart cities. Existing traffic forecasting studies typically assume fixed sensor graphs, overlooking the continuous evolution of real-world traffic networks, e.g., ongoing road network construction and evolving human mobility patterns. These dynamic changes can substantially degrade conventional forecasting models, motivating test-time adaptation (TTA) to efficiently adapt pretrained models during deployment. However, applying TTA to evolving traffic sensor networks remains challenging in two aspects. First, topology expansion introduces new sensors and connections, continuously reshaping the sensor graph. Second, tem- poral shifts vary in time scale and stability, requiring differentiated adaptation to long-term and short-term shifts. In this study, we address these challenges by proposing A2TTA, an Anchored-and-Agile Test-Time Adaptation framework for evolving traffic sensor networks, which transforms topology-induced forecasting errors into an expandable output calibration problem and separates tem- poral adaptation into persistent global correction and agile context-specific specialization. By jointly addressing topology evolution and multi-scale temporal shifts, A2TTA enables efficient and robust adaptation to continuously evolving traffic environments. Extensive experiments on ten real-world traffic networks demonstrate that A2TTA consistently improves forecasting performance across different backbones, datasets, and prediction horizons. Our code is available in https://github.com/lixus7/A2TTA.