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Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models

文献类型：期刊论文

作者Liu, Kang; Jin, Xin4; Cheng, Shifen3; Gao, Song1; Yin, Ling; Lu, Feng3,4
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2023-12-12
卷号N/A
关键词Trajectory generation machine learning mechanistic model human mobility behavior activity sequence
DOI10.1080/13658816.2023.2292570
文献子类Review ; Early Access
英文摘要Human mobility data play a crucial role in many fields such as infectious diseases, transportation, and public safety. Although the development of Information and Communication Technologies (ICTs) has made it easy to collect individual-level positioning records, raw individual trajectory data are still limited in availability and usability due to privacy issues. Developing models to generate synthetic trajectories that are statistically close to the real data is a promising solution. This study proposed a novel trajectory generation method called Act2Loc (Activity to Location), which combined machine learning and mechanistic models. First, an activity-sequence generation model was constructed based on machine learning models (i.e. K-medoids and Transformer) to generate individual activity sequences aligning with human activity patterns. Then, a spatial-location selection model was proposed based on mechanistic models (e.g. Universal Opportunity model) to explicitly determine the specific locations of the activities in each generated sequence. Experimental results showed that compared to baselines based on purely machine learning or mechanistic models, Act2Loc can better reproduce the spatio-temporal characteristics of the real data, with additional advantage of low data requirements for training, proving its potential for generating synthetic trajectories in practice. This research offers new insights on knowledge-guided GeoAI models for human mobility.
WOS关键词HUMAN MOBILITY ; VALIDATION
WOS研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS记录号WOS:001123543100001
源URL[http://ir.igsnrr.ac.cn/handle/311030/200922]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
2.Univ Wisconsin Madison, Dept Geog, Madison, WI USA
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Kang,Jin, Xin,Cheng, Shifen,et al. Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2023,N/A.
APA Liu, Kang,Jin, Xin,Cheng, Shifen,Gao, Song,Yin, Ling,&Lu, Feng.(2023).Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A.
MLA Liu, Kang,et al."Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2023).

入库方式： OAI收割

来源：地理科学与资源研究所

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