Long-Term Traffic Flow Prediction: A Knowledge-Driven Graph Attention Spatio-Temporal Network

As an important research topic in the field of intelligent network management, network traffic prediction has received extensive attention in recent years. However, the existing research mainly focuses on short-term network traffic prediction and does not consider the influence of external factors and the effective embedding of heterogeneous data sources. Effective longterm traffic prediction has become a challenging problem. To address these challenges, this paper proposes a knowledge-driven deep learning method KGASTN for spatio-temporal graphical convolutional networks for long-term traffic flow prediction with multiple factors. In the method, our innovative idea is to design a knowledge-data dual-driven feature extraction scheme that fuses the knowledge representation of external factors into a spatio-temporal graph convolutional network. The method utilizes knowledge inference and sequence similarity algorithms to realize the extraction of explicit knowledge in log data and the construction of similarity graphs between traffic data sequences; and fuses explicit knowledge and similarity relationships based on the knowledge-data dual-drive model, and ultimately constructs spatio-temporal graph convolutional networks based on the attention mechanism. We evaluate KGASTN with a heterogeneous dataset containing log data, and use several sequence prediction datasets from other application domains for additional comparison. Experimental results show that our method outperforms several state-of-the-art baselines.