用共享交通周期模式提升数据少城市的路况预测精度
Urban traffic analysis and forecasting through shared Koopman eigenmodes
- 从多个城市数据中提取通用交通周期模式（城市心跳）
- 在数据稀缺城市上注入这些模式，预测准确率显著提升
- 适合交通管理与城市规划者用于低数据场景
数据稀缺城市的交通流量预测面临挑战，因历史数据有限。为此，我们通过迁移学习，利用定制版动态模态分解（约束汉克尔化DMD，TrHDMD）识别数据丰富城市中的周期性模式。该方法揭示了交通模式中共有的特征值模态（即城市心跳），并将其迁移到数据稀缺城市，显著提升预测性能。TrHDMD通过利用其他城市的先验知识，减少对大规模训练数据的需求。基于多城市环形检测器数据，应用科普曼算子理论识别出稳定、可解释且时间不变的交通模式。将‘城市心跳’注入预测任务后，不仅提高预测准确性，也为不同数据基础设施的城市提供优化交通管理策略的可能。本研究提出通过共享科普曼特征模态实现跨城市知识迁移，为数据匮乏城市提供可操作的洞察与可靠预测。
原文摘要 · Abstract (English)
Predicting traffic flow in data-scarce cities is challenging due to limited historical data. To address this, we leverage transfer learning by identifying periodic patterns common to data-rich cities using a customized variant of Dynamic Mode Decomposition (DMD): constrained Hankelized DMD (TrHDMD). This method uncovers common eigenmodes (urban heartbeats) in traffic patterns and transfers them to data-scarce cities, significantly enhancing prediction performance. TrHDMD reduces the need for extensive training datasets by utilizing prior knowledge from other cities. By applying Koopman operator theory to multi-city loop detector data, we identify stable, interpretable, and time-invariant traffic modes. Injecting ``urban heartbeats'' into forecasting tasks improves prediction accuracy and has the potential to enhance traffic management strategies for cities with varying data infrastructures. Our work introduces cross-city knowledge transfer via shared Koopman eigenmodes, offering actionable insights and reliable forecasts for data-scarce urban environments.
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