A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework

doi:10.1109/TITS.2022.3148358

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
	Jin, Junchen 3; Rong, Dingding 4; Zhang, Tong 5; Ji, Qingyuan 4,6; Guo, Haifeng 7; Lv, Yisheng 8; Ma, Xiaoliang 1,2; Wang, Fei-Yue 8
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2022-02-11
页码	12
关键词	Roads Predictive models Data models Recurrent neural networks Generators Computer architecture Deep learning Short-term link speed prediction signalized urban networks Wasserstein generative adversarial network
ISSN号	1524-9050
DOI	10.1109/TITS.2022.3148358
通讯作者	Ji, Qingyuan(qingyuan.ji@zju.edu.cn)
英文摘要	Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.
资助项目	National Key Research and Development Program of China[2020YFB2104001] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[52072343] ; Zhejiang Provincial Natural Science Foundation (ZJNSF)[LY20E080023] ; iTensor Project by Richterska Stiftelsen[2019-00498] ; iHorse Project by KTH Digital Futures
WOS关键词	STATE ESTIMATION
WOS研究方向	Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000758733600001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Zhejiang Provincial Natural Science Foundation (ZJNSF) ; iTensor Project by Richterska Stiftelsen ; iHorse Project by KTH Digital Futures
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47951]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Ji, Qingyuan
作者单位	1.KTH Royal Inst Technol, Dept Civil & Architecture Engn, Sch Architecture & Bldg Environm, S-10044 Stockholm, Sweden 2.KTH Royal Inst Technol, Ctr Digital Futures, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden 3.Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China 4.Enjoyor Co Ltd, Hangzhou 310030, Peoples R China 5.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China 6.Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China 7.Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310013, Peoples R China 8.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Jin, Junchen,Rong, Dingding,Zhang, Tong,et al. A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:12.
APA	Jin, Junchen.,Rong, Dingding.,Zhang, Tong.,Ji, Qingyuan.,Guo, Haifeng.,...&Wang, Fei-Yue.(2022).A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,12.
MLA	Jin, Junchen,et al."A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):12.