Predicting the Next Location of Urban Individuals via a Representation-Enhanced Multi-View Learning Network
文献类型：期刊论文
| 作者 | Lun, Maoqi2; Wang, Peixiao1,3; Wu, Sheng2; Zhang, Hengcai1,3; Cheng, Shifen1,3; Lu, Feng1,3 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2025-08-02 |
| 卷号 | 14期号:8页码:302 |
| 关键词 | location prediction mobile phone data multi-view learning community detection representation enhancement |
| DOI | 10.3390/ijgi14080302 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. |
| URL标识 | 查看原文 |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001557686300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216026]  |
| 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 | Wu, Sheng |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Fuzhou Univ, Acad Digital China Fujian, Fuzhou 350002, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Lun, Maoqi,Wang, Peixiao,Wu, Sheng,et al. Predicting the Next Location of Urban Individuals via a Representation-Enhanced Multi-View Learning Network[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2025,14(8):302. |
| APA | Lun, Maoqi,Wang, Peixiao,Wu, Sheng,Zhang, Hengcai,Cheng, Shifen,&Lu, Feng.(2025).Predicting the Next Location of Urban Individuals via a Representation-Enhanced Multi-View Learning Network.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,14(8),302. |
| MLA | Lun, Maoqi,et al."Predicting the Next Location of Urban Individuals via a Representation-Enhanced Multi-View Learning Network".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 14.8(2025):302. |
入库方式： OAI收割
来源：地理科学与资源研究所
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