MFTFormer: Meteorological-Frequency-Temporal Transformer with Block-Aligned Fusion for Traffic Flow Prediction
Qiannan Shen, Jing Zhang
Abstract
Urban traffic flow prediction is critical for intelligent transportation systems, yet remains challenging due to complex spatiotemporal dependencies and external weather influences. Existing methods suffer from three key limitations: (1) uniform time-domain modeling fails to disentangle periodic trends from aperiodic fluctuations, (2) weather information is integrated through simple concatenation without considering multi-granularity impacts, and (3) lack of interpretability hinders understanding of weather-traffic interactions. To address these issues, we propose MFTFormer, a novel framework that synergistically combines time-frequency decomposition with hierarchical weather-aware fusion. Our approach introduces three key innovations: (1) a dual-domain encoder with mixture-of-experts that separately processes trend and seasonal components via specialized frequency and temporal attention networks, (2) a block-aligned fusion module that hierarchically reorganizes features through convolutional refinement and weather-conditioned modulation, capturing both immediate and cumulative meteorological effects, and (3) interpretable attention mechanisms that reveal dynamic spatial dependencies under varying weather conditions. Extensive experiments on four real-world datasets (PeMS04, PeMS08, Metro-Traffic, METR-LA) demonstrate that MFTFormer achieves state-of-the-art performance with an average 6.2% MAE reduction over strong baselines, while providing actionable insights into weather-traffic relationships through attention visualization.