Road traffic network state prediction based on a generative adversarial network. Issue 10 (1st October 2020)

• Record Type: Journal Article
• Authors: Xu, Dongwei; Peng, Peng; Wei, Chenchen; He, Defeng; Xuan, Qi
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 10(2020)
• Page Start: 1286
• Page End: 1294
• Publication Date: 2020-10-01
• Subjects: traffic engineering computing -- neural nets -- road traffic control; road traffic network state prediction -- generative adversarial network -- traffic state prediction -- intelligent transportation systems -- complex spatial influence -- traffic networks -- nonstationary temporal nature -- traffic network state prediction model -- generative adversarial framework -- short‐term memory networks -- prediction accuracy -- traffic network states; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0552
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Traffic state prediction plays an important role in intelligent transportation systems, but the complex spatial influence of traffic networks and the non‐stationary temporal nature of traffic states make it a challenging task. In this study, a new traffic network state prediction model for freeways based on a generative adversarial framework is proposed. The generator based on the long short‐term memory networks is adopted to generate future traffic states, and a discriminator with multiple fully connected layers is applied to simultaneously ensure the prediction accuracy. The results of experiments show that the proposed framework can effectively predict future traffic network states and is superior to the baselines.