WR-MDGCN: Wavelet Residual and Multi-Scale Dynamic Graph Convolution Network for Traffic Flow Prediction

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Abstract
Traffic flow prediction is a critical task in Intelligent Transportation Systems (ITS). However, the complex spatiotemporal dynamics, periodicity, and anomalous variations in traffic data pose significant modeling challenges. To address this, this paper proposes a novel Wavelet Residual and Multi-Scale Dynamic Graph Convolution Network (WR-MDGCN). It first applies the discrete wavelet transform (DWT) to decompose traffic signals into low-frequency and high-frequency components, and then performs layer-wise residual refinement to progressively extract spatiotemporally consistent periodic and anomalous patterns, laying a foundation for differentiated modeling. We then construct signal-specific dynamic graphs to enable targeted modeling of fine-grained spatial dependencies. Based on these graphs, WR-MDGCN employs multi-scale dynamic graph convolution to capture both stable patterns and abrupt spatial changes. Periodic signals are modeled via dynamic graph convolution, while anomalous signals are captured using hierarchical diffusion convolution to enhance sensitivity to sudden changes. To adapt to the distinct characteristics of periodic and anomalous signals, a differentiated global information processing mechanism is designed. For periodic signals, the global memory mechanism strengthens long-term dependencies, while for anomalous signals, the dynamic global pooling mechanism considers global dynamic feedback. Experiments on six real-world datasets demonstrate that WR-MDGCN consistently outperforms baselines in both accuracy and stability.

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