Accurate traffic flow prediction remains challenging in cross-city, data-scarce scenarios where limited historical data hinders model generalisation. The chaotic nature of traffic dynamics, complex spatio-temporal dependencies, and heterogeneous urban networks complicate few-shot learning across cities. Existing deep learning approaches either treat traffic as purely deterministic or lack mechanisms to model wave-like interference patterns essential for cross-regime traffic dynamics. To address these limitations, this paper proposes CIWI-CKT, a novel Chaos-Informed Wave Interference Feature Fusion framework with Cross-City Knowledge Transfer. Our framework introduces three core innovations: chaos-informed wave generation that extracts measurable chaos invariants and models traffic as adaptive wave components; meta-interference processing that captures wave interactions between support and query regimes while producing a predictability score for confidence estimation; and chaos-aware meta-learning that enables efficient cross-city knowledge transfer while preserving chaotic characteristics. We establish theoretical guarantees including chaos-to-wave stability, wave-induced dimension reduction, and meta-learning generalisation bounds. Extensive experiments on four real-world traffic datasets demonstrate that CIWI-CKT significantly outperforms state-of-the-art spatio-temporal graph learning, transfer learning, prompt-based, and few-shot methods, improving prediction accuracy while substantially reducing required training data.

翻译：在跨城市、数据稀缺场景下，受限于历史数据不足导致模型泛化能力下降，精准的交通流预测仍面临挑战。交通动态的混沌特性、复杂的时空依赖关系以及异构城市网络，加剧了跨城市少样本学习的难度。现有深度学习方法或视交通为纯确定性过程，或缺乏建模波浪状干涉模式（这对跨状态交通动态至关重要）的机制。针对这些局限，本文提出CIWI-CKT——一种融合混沌信息波干涉特征与跨城市知识迁移的新型框架。该框架包含三大核心创新：混沌信息驱动波生成——提取可测混沌不变量并将交通建模为自适应波分量；元干涉处理——捕捉支持集与查询集状态间的波相互作用，同时生成可预测性分数用于置信度估计；混沌感知元学习——在保留混沌特征的同时实现高效的跨城市知识迁移。我们建立了理论保障，包括混沌-波稳定性、波诱导降维及元学习泛化界。在四个真实交通数据集上的广泛实验表明，CIWI-CKT显著优于当前最先进的时空图学习、迁移学习、提示学习及少样本方法，在提高预测精度的同时大幅减少了所需的训练数据量。