Deep Learning Method for Citywide Crowd Flows Prediction

Crowd flows prediction is an important problem of urban computing. The existing method adopts three deep residual networks to model spatio-temporal properties and achieves good prediction performance. However, since three separated network structures are used to model the properties, the time cost is often expensive for the existing method. In this paper, we propose an improved method to reduce the running time of the existing method by simplifying its architecture. In addition, we apply attention mechanism to make better use of temporal information. As shown in experiments, compared with the existing method, the improved method has significantly reduced running time and achieved better prediction performance.