构建可演化交通传感网络数据集，推动长期交通预测更贴近现实。
From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks
- 设计逐年演化的传感器网络数据结构，模拟真实道路扩展。
- 覆盖27年数据，传感器增长最高达10,000%以上，支持跨年连续预测。
- 适合研究持续学习、动态图神经网络和长期交通预测的学者。
现有交通预测基准假设传感器集合固定，但实际路网随时间持续扩展。本文提出XXLTraffic数据集家族，涵盖加州PeMS与悉尼交通局（Transport for NSW）长达27年的数据。其固定传感器子集支持超长时序预测及多年度间隔预测。进一步构建EvoXXLTraffic，呈现每年活跃传感器、年度流量矩阵与图结构快照，覆盖九个PeMS区域，传感器增长率达+305%至超+10,000%。定义年度流式预测协议，将每一年视为连续任务，评估静态时空GNN、简单在线方法、演化图持续学习模型及检索/测试时方法等代表性基线。结果表明，超大规模演化数据集更贴近现实，许多当前最先进（SOTA）方法失效。该数据集补足现有基准，在超长演化路网下实现更真实的交通预测。代码与基线已开源于GitHub：https://github.com/cruiseresearchgroup/TSAS26-EvoXXLTraffic。
原文摘要 · Abstract (English)
Existing traffic forecasting benchmarks assume a fixed sensor set, but real road-sensor networks grow continuously as the road network changes year by year. We introduce the XXLTraffic dataset family, which spans up to 27 years of California PeMS and Transport for NSW data. The fixed-sensor subsets of XXLTraffic support extremely long forecasting with multi-year gaps and standard hourly / daily long-horizon forecasting. We extend it to EvoXXLTraffic, a sensor-evolving reorganization that exposes per-year active sensors, yearly traffic-flow matrices, and yearly graph snapshots across nine PeMS districts, with growth ratios ranging from +305% to over +10,000%. We define a yearly streaming forecasting protocol on EvoXXLTraffic in which each calendar year is a continual task, and benchmark a wide range of representative baselines drawn from static spatio-temporal GNNs, naïve online schemes, evolving-graph continual methods, and retrieval / test-time methods. We find that our ultra-large evolutionary dataset better reflects the real world, and many state-of-the-art (SOTA) results no longer work. Our dataset complements existing benchmarks by enabling more realistic forecasting under ultra-long evolutionary road networks. Our code and baselines are available at github repo: https://github.com/cruiseresearchgroup/TSAS26-EvoXXLTraffic
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