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Spatial-Temporal Network Data-Driven Multi-Layer Traffic Knowledge Graph Reconstruction for Dynamic Prediction

Spatial-Temporal correlation is the key to the prediction of spatial-temporal network data, and external factors will also affect the prediction results of spatial-temporal network data. Existing traffic flow forecasting methods all capture spatial and temporal dependencies separately in a sequential manner, while spatial-temporal correlations and external condition factors are closely intertwined with traffic data, leading to the problem of forecast bias. In response to the above problems, this paper proposes a ST-KG (Spatial-temporal network data-driven multi-layer traffic knowledge graph reconstruction) traffic prediction method. First, a spatial-temporal map is constructed based on the GCN-GRU (graph convolutional network-gate recurrent unit) model to capture the spatial-temporal correlation, weather, POIS, holidays and other external factors in the local spatial-temporal map. Then the multi-layer traffic knowledge graph reconstruction technology is introduced to reconstruct the model. The three-layer traffic knowledge graph (knowledge graph in the field of transportation) is progressive and complementary to each other, and finally realizes the modeling of the traffic system, so that the nodes of the road segment can perceive each other. The knowledge graph makes more accurate predictions by sensing the information of other nodes as a supplement to the information. Extensive experiments are conducted on the PeMSD4 and PeMSD8 datasets, and the results consistently outperform other baselines, demonstrating the superiority of our method against the problem of prediction bias for spatial-temporal network data.