Road traffic network state prediction based on a generative adversarial network

被引:17
作者
Xu, Dongwei [1 ,2 ]
Peng, Peng [1 ,2 ]
Wei, Chenchen [1 ,2 ]
He, Defeng [2 ]
Xuan, Qi [1 ,2 ]
机构
[1] Zhejiang Univ Technol, Inst Cyberspace Secur, Hangzhou, Peoples R China
[2] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
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; TRAVEL-TIME PREDICTION; FLOW PREDICTION; NEURAL-NETWORK; ARCHITECTURE; VOLUME;
D O I
10.1049/iet-its.2019.0552
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:1286 / 1294
页数:9
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