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  4. 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

文献类型：期刊论文

作者Ren, Yibin1,2,3; Chen, Huanfa4; Han, Yong5,6; Cheng, Tao7; Zhang, Yang7; Chen, Ge5,6
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2019-08-15
页码22
ISSN号1365-8816
关键词Spatio-temporal flow volume prediction deep learning LSTM ResNet
DOI10.1080/13658816.2019.1652303
通讯作者Han, Yong(yonghan@ouc.edu.cn)
英文摘要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.
资助项目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
语种英语
出版者TAYLOR & FRANCIS LTD
WOS记录号WOS:000481199600001
源URL[http://ir.qdio.ac.cn/handle/337002/162330]  
专题海洋研究所_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
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.

入库方式： OAI收割

来源：海洋研究所

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