Attention based simplified deep residual network for citywide crowd flows prediction

Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future. In practice, emergency applications often require less training time. However, there is a little work on how to obtain good prediction performance with less training time. In this paper, we propose a simplified deep residual network for our problem. By using the simplified deep residual network, we can obtain not only less training time but also competitive prediction performance compared with the existing similar method. Moreover, we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time cost. Based on the real datasets, we construct a series of experiments compared with the existing methods. The experimental results confirm the efficiency of our proposed methods.