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Profiling Public Transit Passenger Mobility Using Adversarial Learning

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成果类型:
期刊论文
作者:
Li, Yicong;Zhang, Tong;Lv, Xiaofei;Lu, Yingxi;Wang, Wangshu
通讯作者:
Zhang, T
作者机构:
[Li, Yicong; Lv, Xiaofei] Zhongnan Engn Corp Ltd Power China, Changsha 410014, Peoples R China.
[Zhang, Tong] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China.
[Lu, Yingxi] Hunan City Univ, Coll Architecture & Urban Planning, Yiyang 413000, Peoples R China.
[Wang, Wangshu] TU Wien, Dept Geodesy & Geoinformat, A-1040 Vienna, Austria.
通讯机构:
[Zhang, T ] W
Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
transit mobility embedding;generative adversarial network;smart card data;public transit
期刊:
ISPRS International Journal of Geo-Information
ISSN:
2220-9964
年:
2023
卷:
12
期:
8
页码:
338-
基金类别:
Conceptualization, Yicong Li and Tong Zhang; methodology, Yicong Li and Tong Zhang; software, Yicong Li, Xiaofei Lv, Yingxi Lu and Wangshu Wang; validation, Yicong Li, Xiaofei Lv, Yingxi Lu and Wangshu Wang; formal analysis, Yicong Li, Xiaofei Lv and Yingxi Lu; investigation, Yicong Li, Xiaofei Lv and Yingxi Lu; resources, Tong Zhang; data curation, Xiaofei Lv, Yingxi Lu and Wangshu Wang; writing—original draft preparation, Yicong Li; writing—review and editing, Tong Zhang; visualization, Xiaofei Lv and Yingxi Lu; supervision, Tong Zhang; project administration, Yicong Li; funding acquisition, Tong Zhang. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key R&D Program of China (International Scientific & Technological Cooperation Program), grant number 2019YFE0106500 and the open fund of Wuhan University—Huawei Geoinformatics Innovation Laboratory.
机构署名:
本校为其他机构
院系归属:
建筑与城市规划学院
摘要:
It is important to capture passengers’ public transit behavior and their mobility to create profiles, which are critical for analyzing human activities, understanding the social and economic structure of cities, improving public transportation, assisting urban planning, and promoting smart cities. In this paper, we develop a generative adversarial machine learning network to characterize the temporal and spatial mobility behavior of public transit passengers, based on massive smart card data and road network data. The Apriori algorithm is exte...

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