Accurate traffic flow prediction remains a fundamental challenge in intelligent transportation systems, particularly in cross-domain, data-scarce scenarios where limited historical data hinders model training and generalisation. The complex spatio-temporal dependencies and nonlinear dynamics of urban mobility networks further complicate few-shot learning across different cities. This paper proposes MCPST, a novel Multi-phase Consensus Spatio-Temporal framework for few-shot traffic forecasting that reconceptualises traffic prediction as a multi-phase consensus learning problem. Our framework introduces three core innovations: (1) a multi-phase engine that models traffic dynamics through diffusion, synchronisation, and spectral embeddings for comprehensive dynamic characterisation; (2) an adaptive consensus mechanism that dynamically fuses phase-specific predictions while enforcing consistency; and (3) a structured meta-learning strategy for rapid adaptation to new cities with minimal data. We establish extensive theoretical guarantees, including representation theorems with bounded approximation errors and generalisation bounds for few-shot adaptation. Through experiments on four real-world datasets, MCPST outperforms fourteen state-of-the-art methods in spatio-temporal graph learning methods, dynamic graph transfer learning methods, prompt-based spatio-temporal prediction methods and cross-domain few-shot settings, improving prediction accuracy while reducing required training data and providing interpretable insights. The implementation code is available at https://github.com/afofanah/MCPST.

翻译：精确的交通流预测仍然是智能交通系统中的一个基本挑战，尤其是在跨领域、数据稀缺的场景中，有限的历史数据阻碍了模型的训练和泛化。城市移动网络复杂的时空依赖性和非线性动力学特性，进一步加剧了不同城市间少样本学习的难度。本文提出了MCPST，一种用于少样本交通预测的新型多阶段共识时空框架，该框架将交通预测重新概念化为一个多阶段共识学习问题。我们的框架引入了三个核心创新：(1) 一个多阶段引擎，通过扩散、同步和谱嵌入对交通动力学进行建模，以实现全面的动态特征刻画；(2) 一种自适应共识机制，动态融合特定阶段的预测结果，同时强制保持一致性；(3) 一种结构化的元学习策略，用于以最少的数据快速适应新城市。我们建立了广泛的理论保证，包括具有有界近似误差的表示定理和少样本适应的泛化界。通过在四个真实世界数据集上的实验，MCPST在时空图学习方法、动态图迁移学习方法、基于提示的时空预测方法以及跨领域少样本设置中，均优于十四种最先进的方法，在提高预测精度的同时减少了所需的训练数据，并提供了可解释的见解。实现代码可在 https://github.com/afofanah/MCPST 获取。