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, Yibin1,2,3; Chen, Huanfa4; Han, Yong5,6; Cheng, Tao7; Zhang, Yang7; Chen, Ge5,6
2019-08-15
Source PublicationINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN1365-8816
Pages22
Corresponding AuthorHan, Yong(yonghan@ouc.edu.cn)
AbstractThe 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.
KeywordSpatio-temporal flow volume prediction deep learning LSTM ResNet
DOI10.1080/13658816.2019.1652303
Indexed BySCI
Language英语
Funding ProjectScience and Technology Project of Qingdao[16-6-2-61-NSH] ; China Scholarship Council (CSC)
WOS Research AreaComputer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS SubjectComputer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS IDWOS:000481199600001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/162331
Collection海洋环流与波动重点实验室
Corresponding AuthorHan, Yong
Affiliation1.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
First Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences
Recommended Citation
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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