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

Abstract

Highlights What are the main findings? An enhanced spatio-temporal graph Transformer (STGFormer) is proposed for traffic flow prediction, combining temporal self-attention and learnable temporal encoding to capture both long-term traffic evolution patterns and sudden fluctuations. Weather data are incorporated into the prediction process to achieve robust prediction under complex environmental conditions. What are the implications of the main findings? An effective solution is provided for traffic flow prediction in sparse-sensing scenarios. The applicability of traffic prediction models is extended to complex and heterogeneous real-world environments. Abstract 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.