VHLformer: Decompose Attention Mechanism in Frequency Perspective for Traffic Flow Prediction

As an essential component of intelligent transportation, traffic flow prediction is crucial in decision-making and traffic system optimization. However, traffic flow prediction is a challenging task. Traffic flow is essentially a type of time series with distinct temporal characteristics. Furthermore, due to the mutual influence between traffic nodes, there are complex dependencies in the flow sequences of each node, which contribute to the complexity of traffic flow prediction. This paper primarily analyzes the temporal dimension of traffic flow, which can be decomposed into different frequencies. Existing models often fail to handle specific frequency bands, leading to bottlenecks in prediction performance. In recent years, Transformers have proven to possess powerful sequence modelling capabilities. However, the self-attention mechanism is insensitive to high-frequency information, and the loss of such information results in decreased prediction performance. To address the aforementioned issues, we proposed decomposing the multi-head attention mechanism, with different attention heads handling information of different frequencies in traffic flow, and named it VHL-Attention. Based on VHL-Attention, we established VHLformer. Experiments on real-world datasets demonstrate that VHLformer achieves the state-of-the-art performance.