Traffic Condition Prediction of Urban Roads Based on Neural Network

Real-time and reliable traffic flow estimation is the basis of urban traffic management and control. However, the existing research focuses on how to use the historical data of surveillance intersection to predict future traffic conditions. As we know, there are few effective algorithms to infer the real-time traffic state of non-surveillance intersections from limited road surveillance by using traffic information in the urban road system. In this paper, we introduce a new solution to solve the prediction task of traffic flow analysis by using traffic data, especially taxi historical data, traffic network data and intersection historical data. The proposed solution takes advantage of GCN and CGAN, and we improved the Unet to realize an important part of the generator. Then, we capture the relationship between the intersections with surveillance and the intersections without surveillance by floating taxi-cabs covered in the whole city. The framework of CGAN can adjust the weights and enhance the inference ability to generate complete traffic status under current conditions. The experimental results show that our method is superior to other methods on the accuracy of traffic volume inference.