The accurate prediction of crowd flow in urban areas is becoming more and more important in many fields such as traffic management and public safety. However, the complex spatiotemporal relationship of the traffic data and the influence of events, weather, and other factors makes it very difficult to accurately predict the crowd flow. In this study, we propose a spatiotemporal prediction model that is based on densely connected convolutional networks and gated recurrent units (GRU) with the attention mechanism to predict the inflow and outflow of the crowds in regions within a specific area. The DCAST model divides the time axis into three parts: short-term dependence, period rule, and long-term dependence. For each part, we employ densely connected convolutional networks to extract spatial characteristics. Attention-based GRU module is used to capture the temporal features. And then, the outputs of the three parts are fused by weighting elementwise addition. At last, we combine the results of the fusion and external factors to predict the crowd flow in each region. The root mean square errors of the DCAST model in two real datasets of taxis in Beijing (TaxiBJ) and bikes in New York (BikeNYC) are 15.70 and 5.53, respectively. The experimental results show that the results are more accurate and reliable than that of the baseline model.