A road network traffic flow data imputation method based on the fusion of spatiotemporal features and adversarial networks

Abstract

In response to the problem of missing traffic flow data on highways, to solve the problem of insufficient mining of traffic flow characteristics using existing spatiotemporal correlation repair methods, a missing data repair method based on spatiotemporal fusion adversarial network is proposed based on analyzing the spatiotemporal characteristics of traffic flow, this method utilizes the fusion of GRU and GCN to capture the fine-grained spatiotemporal relationships of traffic flow, optimizes the generator and discriminator of the adversarial network, and achieves accurate repair of missing data. Experiments based on real data have shown that under various missing modes and rates, the model can learn the topological relationships of the road network, capture the temporal regularity and spatiotemporal correlation in the data, and effectively impute missing data.