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DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending

文献类型：期刊论文

作者Dai, Xingyuan1,2,4; Fu, Rui3; Zhao, Enmin3; Zhang, Zuo3; Lin, Yilun1,2,4; Wang, Fei-Yue1,2,4; Li, Li3
刊名TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
出版日期2019-06-01
卷号103页码:142-157
关键词Traffic prediction Deep learning Detrending Multi-scale traffic prediction
ISSN号0968-090X
DOI10.1016/j.trc.2019.03.022
通讯作者Li, Li(li-li@tsinghua.edu.cn)
英文摘要In this paper, we propose a detrending based and deep learning based many-to-many traffic prediction model called DeepTrend 2.0 that accepts information collected from multiple sensors as input and simultaneously generates the prediction for all the sensors as output. First, we demonstrate that detrending brings advantages to traffic prediction, even when deep learning models are considered. Second, the proposed model strikes a delicate balance between model complexity and accuracy. In contrast to the existing models that view a sensor network as a weighted graph and use graph convolutional neural networks (GCNN) to model spatial dependency, we represent a sensor network as an image and propose a convolutional neural network (CNN) as the prediction model. The image is generated by the correlation coefficient between the flow series of sensors, which is different from other CNN based prediction approaches that convert the transportation network into an image by the spatial location of sensors or regions. Compared with the GCNN based model, the CNN based DeepTrend 2.0 can achieve much faster convergence during training, and it guarantees similar prediction quality. Test results indicate that the proposed light-weighted model is efficient and easy to transfer and deploy.
WOS关键词FLOW PREDICTION ; NEURAL-NETWORK ; VOLUME
资助项目National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[U1811463] ; Beijing Municipal Science and Technology Commission Program[D171100000317002] ; Beijing Municipal Commission of Transport Program[ZC179074Z]
WOS研究方向Transportation
语种英语
WOS记录号WOS:000471361900009
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission Program ; Beijing Municipal Commission of Transport Program
源URL[http://ir.ia.ac.cn/handle/173211/26068]  
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Li, Li
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
4.Qingdao Acad Intelligent Ind, Qingdao 266109, Shandong, Peoples R China
推荐引用方式
GB/T 7714		 Dai, Xingyuan,Fu, Rui,Zhao, Enmin,et al. DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,2019,103:142-157.
APA Dai, Xingyuan.,Fu, Rui.,Zhao, Enmin.,Zhang, Zuo.,Lin, Yilun.,...&Li, Li.(2019).DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending.TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,103,142-157.
MLA Dai, Xingyuan,et al."DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending".TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 103(2019):142-157.

入库方式： OAI收割

来源：自动化研究所

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