发表机构
University of New South Wales; Griffith University; Beihang University; RMIT University（新南威尔士大学; 格里菲斯大学; 北京航空航天大学; 皇家墨尔本理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对交通传感器网络动态变化致传统预测模型性能下降问题，提出A2TTA框架，将拓扑诱导误差转化为输出校准问题，区分时间自适应，联合应对拓扑演变和多尺度时间偏移，实验证明该框架能有效提升预测性能。
AI 中文摘要
交通预测对智慧城市的高效交通管理和路线规划至关重要。现有研究通常假定传感器图固定，忽视了现实交通网络的持续演变。这些动态变化会严重降低传统预测模型的性能，促使进行测试时自适应（TTA）。然而，将TTA应用于不断发展的交通传感器网络在两方面仍具挑战：拓扑扩展会引入新传感器和连接，持续重塑传感器图；时间偏移在时间尺度和稳定性上各不相同，需要对长期和短期偏移进行差异化自适应。本研究提出A2TTA框架来应对这些挑战，它将拓扑诱导的预测误差转化为可扩展的输出校准问题，并将时间自适应分为持久的全局校正和敏捷的特定上下文专业化。通过联合处理拓扑演变和多尺度时间偏移，A2TTA能高效且稳健地适应不断变化的交通环境。在十个真实世界交通网络上的大量实验表明，A2TTA在不同骨干网络、数据集和预测范围内持续提高预测性能。
英文摘要
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.
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