Open AccessRoad traffic network state prediction based on a generative adversarial network
Author(s) -
Xu Dongwei,
Peng Peng,
Wei Chenchen,
He Defeng,
Xuan Qi
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0552
Subject(s) - discriminator , computer science , state (computer science) , generator (circuit theory) , traffic generation model , adversarial system , task (project management) , generative grammar , artificial intelligence , intelligent transportation system , term (time) , machine learning , data mining , real time computing , engineering , transport engineering , algorithm , telecommunications , power (physics) , physics , systems engineering , quantum mechanics , detector
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.