Traffic flow prediction via spatiotemporal trend-event decomposition graph convolutional networks

Abstract

Traffic flow prediction remains challenging due to the complex interaction between heterogeneous temporal frequencies and irregular spatial structures. Most existing graph neural network (GNN)-based methods fail to explicitly model spatiotemporal dependencies across multiple frequency components in traffic flow. To address this limitation, a novel framework, termed Spatiotemporal Trend-Event Decomposition Graph Convolutional Network (STEDGCN), is proposed. The framework introduces a temporal signal separator that decomposes raw traffic flow sequences into low-frequency trends and high-frequency events, thereby preserving frequency-specific temporal patterns. A dual-frequency spatiotemporal encoder is designed to model the temporal and spatial characteristics of the two components. It integrates multi-head attention and causal convolution to model temporal dynamics. It also employs trend-driven and event-driven graphs to capture inter-node dependencies and spatiotemporal correlations. A fusion-gated spatiotemporal decoder is introduced to reduce channel redundancy using a gating mechanism. It enables information interaction between the trend and event branches through a query-driven attention strategy. This improves the coherence of the final prediction. Experiments on four benchmark traffic flow datasets show that the proposed model consistently outperforms state-of-the-art methods across multiple metrics. These results confirm the effectiveness of frequency-aware decoupling and dual-path fusion in complex traffic flow modeling.