针对跨城市交通预测难题，提出感知混沌特性的知识迁移框架。
CAST-CKT: Chaos-Aware Spatio-Temporal and Cross-City Knowledge Transfer for Traffic Flow Prediction
- 通过混沌分析量化交通可预测性，自适应建模时空动态
- 在四个跨城市数据稀缺场景中，MAE和RMSE显著优于现有方法
- 支持分时预测与不确定性估计，适合城市交通系统研究者
在数据稀疏的跨城市交通预测中，复杂非线性动态与领域差异带来挑战。现有方法难以捕捉交通固有的混沌特性，影响少样本学习效果。本文提出CAST-CKT框架，利用高效的混沌分析器量化交通可预测性状态，驱动多项创新：基于混沌感知的注意力机制实现时序建模的自适应；动态拓扑学习捕捉空间依赖关系；基于混沌一致性的跨城市对齐促进知识迁移。框架还提供分时预测与不确定性量化。理论分析表明其泛化能力提升。在四个跨城市少样本基准上实验显示，该方法在MAE和RMSE上显著优于当前最优模型，同时具备可解释的可预测性分析。代码已开源。
原文摘要 · Abstract (English)
Traffic prediction in data-scarce, cross-city settings is challenging due to complex nonlinear dynamics and domain shifts. Existing methods often fail to capture traffic's inherent chaotic nature for effective few-shot learning. We propose CAST-CKT, a novel Chaos-Aware Spatio-Temporal and Cross-City Knowledge Transfer framework. It employs an efficient chaotic analyser to quantify traffic predictability regimes, driving several key innovations: chaos-aware attention for regime-adaptive temporal modelling; adaptive topology learning for dynamic spatial dependencies; and chaotic consistency-based cross-city alignment for knowledge transfer. The framework also provides horizon-specific predictions with uncertainty quantification. Theoretical analysis shows improved generalisation bounds. Extensive experiments on four benchmarks in cross-city few-shot settings show CAST-CKT outperforms state-of-the-art methods by significant margins in MAE and RMSE, while offering interpretable regime analysis. Code is available at https://github.com/afofanah/CAST-CKT.
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