SSGCRTN: a space-specific graph convolutional recurrent transformer network for traffic prediction

Current research often formalizes traffic prediction tasks as spatio-temporal graph modeling problems. Despite some progress, this approach still has the following limitations. First, space can be divided into intrinsic and latent spaces. Static graphs in intrinsic space lack flexibility when facing changing prediction tasks, while dynamic relationships in latent space are influenced by multiple factors. A deep understanding of specific traffic patterns in different spaces is crucial for accurately modeling spatial dependencies. Second, most studies focus on correlations in sequential time periods, neglecting both reverse and global temporal correlations. This oversight leads to incomplete temporal representations in models. In this work, we propose a Space-Specific Graph Convolutional Recurrent Transformer Network (SSGCRTN) to address these limitations simultaneously. For the spatial aspect, we propose a space-specific graph convolution operation to identify patterns unique to each space. For the temporal aspect, we introduce a spatio-temporal interaction module that integrates spatial and temporal domain knowledge of nodes at multiple granularities. This module learns and utilizes parallel spatio-temporal relationships between different time points from both forward and backward perspectives, revealing latent patterns in spatio-temporal associations. Additionally, we use a transformer-based global temporal fusion module to capture global spatio-temporal correlations. We conduct experiments on four real-world traffic flow datasets (PeMS03/04/07/08) and two traffic speed datasets (PeMSD7(M)/(L)), achieving better performance than existing technologies. Notably, on the PeMS08 dataset, our model improves the MAE by 6.41% compared to DGCRN. The code of SSGCRTN is available at https://github.com/OvOYu/SSGCRTN.