STGFormer: Spatio-Temporal Graph Transformer for Traffic Flow Prediction in Sparse-Sensing Scenarios

With the continuous improvement in intelligent transportation and data perception levels, determining how to achieve high-precision and generalizable traffic flow prediction based on historical traffic data has become an important issue in intelligent highway management. The Transformer model, with its strong temporal modeling capabilities, has gradually become a research hotspot in time series prediction. However, its original structure has certain limitations in modeling spatial dependencies, making it difficult to fully exploit the topological relationships of the traffic network, and it has weak adaptability to external environmental changes. To address these issues, this paper proposes an improved Transformer prediction model that integrates a graph convolutional network (GCN) and a self-attention mechanism. The model captures spatial topological information through the GCN module, introduces temporal self-attention mechanism and temporal encoding to enhance the modeling of temporal features, and combines weather factors to achieve perception modeling of external disturbances. In the experimental design, considering the uneven distribution of perception resources in reality, the model input only uses the historical traffic data of some nodes, and different node coverage rates are set to test the performance of the model under sparse input conditions. The results show that the model can maintain good accuracy and stability under multiple coverage rates, verifying the effectiveness and application prospects of the structural improvement.