A2TTA 能应对道路扩建和出行变化导致的预测不准，在 10 个真实网络测试中效果明显，搞交通预测的可以看看。
A2TTA 提出了一种针对动态交通传感器网络的测试时自适应框架，解决拓扑扩展（新增传感器和连接）和多尺度时间偏移（长期与短期变化）两大挑战。该方法将拓扑引起的预测误差转化为可扩展的输出校准问题，并将时间适应分解为持久全局校正和敏捷上下文特化。在 10 个真实交通网络上的实验表明，A2TTA 在不同主干网络、数据集和预测时域下均能持续提升预测性能。代码已开源。
A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networks
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.