Spatio-temporal knowledge embedding via circular correlation: insights into functional urban area travel pattern mining

In recent urban studies, understanding the flow patterns of urban residents has become crucial for effective transportation planning and business district design. Traditional data-driven approaches have provided insights but often lead to random and uninterpretable results due to their sole reliance on data features, lacking a deeper contextual and semantic analysis of the underlying patterns. To overcome these limitations, our work introduces a novel framework that fuses holographic knowledge embedding with graph deep learning to predict urban population travel patterns. This dual-driven approach of data and knowledge uniquely integrates traffic geographic information, vehicle trajectory data, and Points of Interest (POI) into a comprehensive urban traffic knowledge graph. Our method not only captures the spatial-temporal dependencies of big data traffic but also models the relationships between geographic, semantic POI information, and urban travel behaviors. The knowledge graph is then processed through a graph deep learning model, enhancing the embedding features and enabling sophisticated link prediction. Compared with conventional data-driven methods, our approach demonstrates significant advancements in harnessing semantic information, leading to more accurate and interpretable predictions of travel patterns. Experimental validation on real-world datasets confirms the effectiveness of our method in capturing complex urban dynamics.