PIMCST: Physics-Informed Multi-Phase Consensus and Spatio-Temporal Few-Shot Learning for Traffic Flow Forecasting

2026-02-02

AI Summary

This work addresses the challenge of low prediction accuracy and poor generalization in traffic flow forecasting under cross-city and data-scarce scenarios. The authors propose a multi-stage consensus learning framework that integrates diffusion processes, synchronization dynamics, and spectral embeddings to construct a dynamic multi-stage model. An adaptive consensus mechanism is designed to fuse predictions from different stages, while a structured meta-learning strategy enables effective few-shot cross-domain transfer, accompanied by theoretical guarantees. Extensive experiments on four real-world datasets demonstrate that the proposed method significantly outperforms 14 state-of-the-art models, achieving higher prediction accuracy and interpretability while substantially reducing the required amount of training data.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• traffic flow forecasting
• few-shot learning
• cross-domain
• spatio-temporal dependencies
• data scarcity

Innovation

• Multi-phase Consensus
• Spatio-Temporal Few-Shot Learning
• Adaptive Consensus Mechanism
• Structured Meta-Learning
• Traffic Flow Forecasting