It is very challenging to estimate the traffic flow before urban road deployment. To solve this problem, this study proposes a new conditional urban traffic generating adversarial network (Curb-GAN) model, which utilizes a conditional generating adversarial network (CGAN) to generate urban traffic flow data. Firstly, the distance relationship and external feature information of each node of the road network are treated as conditions to control the generated results. Secondly, the spatial autocorrelation of the road network is captured by the graph convolutional network (GCN), and the time dependence of traffic in different time slots is captured by the self-attention (SA) mechanism and gated cycle unit (GRU). Finally, the trained generator generates traffic flow data. A large number of experiments on two real spatiotemporal datasets show that the estimation accuracy of the Curb-GAN model is superior to the main baseline methods and can produce more meaningful estimates.