CIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting

• URL: http://arxiv.org/abs/2606.15642v1
• Date: Sun, 14 Jun 2026 07:08:24 GMT
• Title: CIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting
• Abstract summary: CIWI-CKT is a novel Chaos-Informed Wave Interference Feature Fusion framework with Cross-City Knowledge Transfer.<n>Our framework introduces three core innovations: chaos-informed wave generation, meta-interference processing, and chaos-aware meta-learning.<n>Experiments on four real-world traffic datasets demonstrate that CIWI-CKT significantly outperforms state-of-the-art graph learning, transfer learning, prompt-based, and few-shot methods.
• Score: 5.709363708126687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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