Multi-Branch Spatial-Temporal Decoupling Neural Network for Traffic Forecasting

Traffic forecasting has numerous application scenarios, but it is also a challenging task considering complex spatial and temporal correlations. Deep learning has shown great potential in recent years. Graph convolutional networks, recurrent neural networks and other deep learning architectures have been used in traffic forecasting. However, most existing methods capture spatial and temporal correlations sequentially or synchronously and couple spatial and temporal representations, which may introduce spurious dependencies. The heterogeneity of spatio-temporal data also increases the difficulty of modeling and makes it harder to obtain accurate predictions. In this paper, we propose a novel multi-branch spatial-temporal decoupling neural network (MBSTDN) for traffic forecasting. Specifically, we independently map hidden features into spatial and temporal representations and process them through corresponding branches to produce partial predictions. The partial predictions are then aggregated into final predictions by residual links. At the same time, we reconstruct input signals through a separate branch, introducing layer-by-layer decomposition based on residual learning to deal with the intricate dependencies. Furthermore, we design an embedding layer utilizing multi-dimensional information to reduce the influence of spatio-temporal heterogeneity on predictions. Experiments on four public traffic datasets demonstrate the effectiveness of MBSTDN which performs better than the state-of-the-art baselines.