CASIA OpenIR
DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending
Dai, Xingyuan1,2,4; Fu, Rui3; Zhao, Enmin3; Zhang, Zuo3; Lin, Yilun1,2,4; Wang, Fei-Yue1,2,4; Li, Li3
Source PublicationTRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
ISSN0968-090X
2019-06-01
Volume103Pages:142-157
Corresponding AuthorLi, Li(li-li@tsinghua.edu.cn)
AbstractIn 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.
KeywordTraffic prediction Deep learning Detrending Multi-scale traffic prediction
DOI10.1016/j.trc.2019.03.022
WOS KeywordFLOW PREDICTION ; NEURAL-NETWORK ; VOLUME
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission Program ; Beijing Municipal Commission of Transport Program
WOS Research AreaTransportation
WOS SubjectTransportation Science & Technology
WOS IDWOS:000471361900009
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26069
Collection中国科学院自动化研究所
Corresponding AuthorLi, Li
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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.
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