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STAGE: a spatiotemporal-knowledge enhanced multi-task generative adversarial network (GAN) for trajectory generation

文献类型：期刊论文

作者Cao, Zhongcai4,5; Liu, Kang4; Jin, Xin3,4; Ning, Li2; Yin, Ling4; Lu, Feng1,3
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2024-07-27
卷号N/A页码:28
关键词Trajectory generation generative adversarial network multi-task learning spatial consistency loss knowledge-guided GeoAI
ISSN号1365-8816
DOI10.1080/13658816.2024.2381146
英文摘要Individual trajectory data play a pivotal role in various application fields, such as urban planning, traffic control, and epidemic simulation. Despite the diverse means for data collection in current times, the real-world trajectory data in practical application remains severely limited due to concerns over personal privacy. In this study, we designed a Spatiotemporal-knowledge enhanced multi-TAsk GEnerative adversarial network (GAN), named STAGE, to generate synthetic trajectories that statistically resemble the real data without recycling personal information. In STAGE, we designed a multi-task generator with three stages of spatio-temporal generation tasks, i.e. activity-sequence generation task, township-level trajectory generation task, and neighborhood-level trajectory generation task, with the last one as the main task while the other two as auxiliary tasks. Meanwhile, we designed a spatial consistency loss in the adversarial training process to assess the spatial consistency of generated trajectories at different spatial scales. Experiment results show that compared to the baselines, trajectories generated by our method have closer data distributions to the real ones. We argued that the designs of spatiotemporal-knowledge enhanced generation tasks and training loss benefit the spatiotemporal generation processes, which help reproduce the temporal patterns of human daily activities and spatial distribution of human movements.
WOS关键词ANOMALY DETECTION
资助项目National Key Research and Development Program of China
WOS研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种英语
WOS记录号WOS:001279427600001
出版者TAYLOR & FRANCIS LTD
资助机构National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/207118]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Liu, Kang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
2.Univ Elect Sci & Technol China, Shenzhen Inst Adv Study, Shenzhen, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
5.Southern Univ Sci & Technol, Shenzhen, Peoples R China
推荐引用方式
GB/T 7714		 Cao, Zhongcai,Liu, Kang,Jin, Xin,et al. STAGE: a spatiotemporal-knowledge enhanced multi-task generative adversarial network (GAN) for trajectory generation[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2024,N/A:28.
APA Cao, Zhongcai,Liu, Kang,Jin, Xin,Ning, Li,Yin, Ling,&Lu, Feng.(2024).STAGE: a spatiotemporal-knowledge enhanced multi-task generative adversarial network (GAN) for trajectory generation.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A,28.
MLA Cao, Zhongcai,et al."STAGE: a spatiotemporal-knowledge enhanced multi-task generative adversarial network (GAN) for trajectory generation".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2024):28.

入库方式： OAI收割

来源：地理科学与资源研究所

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