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Discovering urban mobility structure: a spatio-temporal representational learning approach

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成果类型:
期刊论文
作者:
Duan, Xiaoqi;Zhang, Tong;Xu, Zhibang;Wan, Qiao;Yan, Jinbiao;...
通讯作者:
Duan, XQ
作者机构:
[Wan, Qiao; Duan, Xiaoqi; Tian, Youliang] Guizhou Univ, Coll Comp Sci & Technol, Guiyang, Peoples R China.
[Zhang, Tong] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China.
[Xu, Zhibang] Chinese Acad Sci, Inst Urban Environm, Xiamen, Peoples R China.
[Yan, Jinbiao] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang, Peoples R China.
[Wang, Wangshu] TUWien, Dept Geodesy & Geoinformat, Vienna, Austria.
通讯机构:
[Duan, XQ ] G
Guizhou Univ, Comp Sci & Technol Inst, Guiyang 550025, Peoples R China.
语种:
英文
关键词:
Urban mobility structure;representational learning;individual travel;spatio-temporal heterogeneity
期刊:
国际数字地球学报(英文)
ISSN:
1753-8947
年:
2023
卷:
16
期:
2
页码:
4044-4072
基金类别:
The authors would like to thank the data distribution agencies for providing the test data and prof. Youliang Tian for his guidance and financial support. The numerical calculations in this paper have been done on the supercomputing system in the Supercomp
机构署名:
本校为其他机构
院系归属:
地理与旅游学院
摘要:
The urban mobility structure is a summary of individual movement patterns and the interaction between persons and the urban environment, which is extremely important for urban management and public transportation route planning. The majority of current research on urban mobility structure discovery utilizes the urban environment as a static network to detect the relationship between people groups and urban areas, ignoring the vital problem of how individuals affect urban mobility structure dynamically. In this paper, we propose a spatio-temporal representational learning method based on reinfo...

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