GE-GAN: A novel deep learning framework for road traffic state estimation

Abstract

Traffic state estimation is a crucial elemental function in Intelligent Transportation Systems (ITS). However, the collected traffic state data are often incomplete in the real world. In this paper, a novel deep learning framework is proposed to use information from adjacent links to estimate road traffic states. First, the representation of the road network is realized based on graph embedding (GE). Second, with this representation information, the generative adversarial network (GAN) is applied to generate the road traffic state information in real-time. Finally, two typical road networks in Caltrans District 7 and Seattle area are adopted as cases study. Experimental results indicate that the estimated road traffic state data of the detectors have higher accuracy than the data estimated by other models.