A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

doi:10.1080/13658816.2019.1652303

IOCAS-IR > 海洋环流与波动重点实验室

	A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes
	Ren, Yibin 1,2,3; Chen, Huanfa 4; Han, Yong 5,6; Cheng, Tao 7; Zhang, Yang 7; Chen, Ge 5,6
	2019-08-15
发表期刊	INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN	1365-8816
页码	22
通讯作者	Han, Yong(yonghan@ouc.edu.cn)
摘要	The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.
关键词	Spatio-temporal flow volume prediction deep learning LSTM ResNet
DOI	10.1080/13658816.2019.1652303
收录类别	SCI
语种	英语
资助项目	Science and Technology Project of Qingdao[16-6-2-61-NSH] ; China Scholarship Council (CSC) ; Science and Technology Project of Qingdao[16-6-2-61-NSH] ; China Scholarship Council (CSC)
WOS研究方向	Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS类目	Computer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS记录号	WOS:000481199600001
出版者	TAYLOR & FRANCIS LTD
引用统计	被引频次：44[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/162331
专题	海洋环流与波动重点实验室
通讯作者	Han, Yong
作者单位	1.Chinese Acad Sci, CAS Key Lab Ocean Circulat & Waves, Inst Oceanol, Qingdao, Shandong, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao, Shandong, Peoples R China 3.Qingdao Natl Lab Marine, Pilot Natl Lab Marine Sci & Technol, Qingdao, Shandong, Peoples R China 4.UCL, Ctr Adv Spatial Anal, London, England 5.Ocean Univ China, Coll Informat Sci & Engn, Qingdao Collaborat Innovat Ctr Marine Sci & Techn, Qingdao, Shandong, Peoples R China 6.Qingdao Natl Lab Marine, Lab Reg Oceanog & Numer Modeling, Qingdao, Shandong, Peoples R China 7.UCL, Dept Civil Environm & Geomat Engn, SpaceTimeLab, London, England
第一作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Ren, Yibin,Chen, Huanfa,Han, Yong,et al. A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2019:22.
APA	Ren, Yibin,Chen, Huanfa,Han, Yong,Cheng, Tao,Zhang, Yang,&Chen, Ge.(2019).A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,22.
MLA	Ren, Yibin,et al."A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2019):22.

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