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 |
ISSN | 0968-090X |
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 |
DOI | 10.1016/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 | |
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 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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