Low-high frequency network for spatial–temporal traffic flow forecasting

Traffic flow forecasting is crucial for smart city development. Existing methods primarily focus on spatial–temporal correlation learning but often overlook the distinct temporal characteristics of traffic flow. From the temporal perspective, traffic flow can be decomposed into low frequency components-representing periodic patterns inherent in the transportation system and high frequency components-reflecting short-term variations caused by external factors. However, current approaches tend to capture high frequency components while neglecting low frequency ones, which has resulted in a performance bottleneck. To address this problem, we propose a novel framework, named Low-High Frequency Network(LHFNet), for traffic flow forecasting. Our framework comprises three key components: a low frequency encoder, a high frequency encoder, and a frequency feature fusion block. The low frequency encoder employs bi-level routing attention as its core module. To enhance the stability of low frequency representations, patch embedding and patch merging operations are integrated, and the benefit of this integration is that the model complexity can be reduced while enabling longer input series. For the high frequency encoder, multilayer perceptrons are used with residual connections as the primary structure. Finally, the frequency feature fusion block dynamically integrates both frequency features through a gated selection mechanism. Extensive comparative experiments have been conducted on four real-world datasets and the results have demonstrated that LHFNet outperforms state-of-the-art models.