GE-GAN: A novel deep learning framework for road traffic state estimation. (August 2020)

• Record Type: Journal Article
• Authors: Xu, Dongwei; Wei, Chenchen; Peng, Peng; Xuan, Qi; Guo, Haifeng
• Is Part Of: Transportation research. Volume 117(2020)
• Publication Date: 2020-08
• Subjects: Traffic state estimation -- Graph embedding -- Generative adversarial network -- Deepwalk; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102635
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: DeepWalk is used for graph embedding of the road network. Based on the results of DeepWalk, GAN is applied to generate road traffic states. The road traffic state estimation results based on GE_GAN have higher accuracy. 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.