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
发表期刊TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
ISSN0968-090X
2019-06-01
卷号103页码:142-157
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.

关键词Traffic prediction Deep learning Detrending Multi-scale traffic prediction
DOI10.1016/j.trc.2019.03.022
关键词[WOS]FLOW PREDICTION ; NEURAL-NETWORK ; VOLUME
收录类别SCI
语种英语
资助项目Beijing Municipal Commission of Transport Program[ZC179074Z] ; Beijing Municipal Science and Technology Commission Program[D171100000317002] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61533019] ; 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
WOS类目Transportation Science & Technology
WOS记录号WOS:000471361900009
出版者PERGAMON-ELSEVIER SCIENCE LTD
七大方向——子方向分类平行管理与控制
国重实验室规划方向分类实体人工智能系统决策-控制
引用统计
被引频次:53[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/26069
专题复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
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
第一作者单位中国科学院自动化研究所
推荐引用方式
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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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