Spatial-Temporal Correlation Learning for Traffic Demand Prediction

Traffic demand prediction has been drawing increasing research interest due to its critical role in intelligent transportation systems. However, conventional deep learning methods for traffic demand forecast ignore the correlations between the pick-up and drop-off demands, thus not fully exploring the patterns of demand evolution. In this work, the pick-up and drop-off demands are treated as two modalities, and an architecture is designed to explicitly model the interactions between the pick-up and drop-off demands both spatially and temporally. Specifically, the self-attention mechanism is adopted to automatically discover spatio-temporal patterns without manual designation for each demand. Then, the cross-attention mechanism is utilized to let the two demands attend to each other, resulting in information exchange between the two demands. The self-attention and cross-attention are combined to capture spatio-temporal correlations simultaneously. Finally, experiments are carried out on three real-world datasets, NYC Citi Bike, NYC Taxi, and BJ Subway, and the results show that this newly proposed method outperforms the state-of-the-art methods.