关键词: ST-DCGAN网络, 时序数据, 缺失交通流量补全

Abstract:

As a new type of time series city data, time series traffic flow data is of great significance to the development of intelligent transportation and smart city. However, due to various reasons, the collected traffic data has a large number of missing, so how to effectively supplement the missing traffic data becomes an urgent problem. The ST-DCGAN model proposed in this paper utilizes the idea of DCGAN network, introduces the complete loss function and the discriminant loss function as the new objective function of the model, and learns the spatio-temporal characteristics between the regional traffic data through the principle of the game between the generator and the discriminator. On the basis of the conventional missing data completion, the data generation idea is used to complete the regional time series traffic flow data, so as to propose a new complement method for the traffic flow missing value. Based on the Beijing TaxiBJ GPS open source dataset, the RMSE evaluation function is used to analyze the effect of the above algorithm on the missing traffic flow completion. The experimental results show that the proposed method is better than compare complementation method.

Key words: ST-DCGAN network, time series, missing traffic flow completion