Spatio-Temporal Multi-Graph Convolution Traffic Flow Prediction Model Based on Multi-Source Information Fusion and Attention Enhancement

Abstract

Featured Application: MIFA-ST-MGCN has significant potential applications in intelligent transportation systems (ITS), particularly for real-time traffic flow prediction and urban traffic management. By integrating multi-source data such as weather conditions and Points of Interest (POI), the model can provide more accurate and adaptive traffic forecasting, essential for optimizing traffic signal control, route planning, and congestion management. Additionally, its robustness to noisy data makes it ideal for deployment in dynamic, real-world environments where data quality can fluctuate. The ability to dynamically weight temporal and spatial features allows for more precise traffic predictions, which can directly improve the efficiency and safety of urban transportation systems, reduce congestion, and contribute to sustainable urban planning. Traffic flow prediction plays a vital role in intelligent transportation systems, directly affecting travel scheduling, road planning, and traffic management efficiency. However, traditional methods often struggle to capture complex spatiotemporal dependencies and integrate heterogeneous data sources. To overcome these challenges, we propose a Spatio-temporal Multi-graph Convolution Traffic Flow Prediction Model based on Multi-source Information Fusion and Attention Enhancement (MIFA-ST-MGCN). The model adopts adaptive data fusion strategies according to spatiotemporal characteristics, achieving effective integration through feature concatenation and multi-graph structure construction. A spatiotemporal attention mechanism is designed to dynamically capture the varying contributions of different adjacency relations and temporal dependencies, thereby enhancing feature representation. In addition, recurrent units are combined with graph convolutional networks to model spatiotemporal data and generate more accurate prediction results. Experiments conducted on a real-world traffic dataset demonstrate that the proposed model achieves superior performance, reducing the mean absolute error by 3.57% compared with mainstream traffic flow prediction models. These results confirm the effectiveness of multi-source fusion and attention enhancement in improving prediction accuracy.