中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 资源与环境信息系统国家重点实验室
  5. 外文论文
DeepIndoorCrowd: Predicting crowd flow in indoor shopping malls with an interpretable transformer network

文献类型：期刊论文

作者Chu, Chen1; Zhang, Hengcai1,3; Wang, Peixiao; Lu, Feng1
刊名TRANSACTIONS IN GIS
出版日期2023-08-30
卷号N/A
ISSN号1361-1682
DOI10.1111/tgis.13095
产权排序1
文献子类Article ; Early Access
英文摘要Accurate and interpretable prediction of crowd flow would benefit business management and public security. The existing studies are challenged to adapt to the indoor environment due to its complex and dynamic spatial interaction patterns. In this study, we propose a crowd flow predicting method for indoor shopping malls, which simultaneously features temporal variables and semantic factors to suit the shopping mall environment. A deep learning model named DeepIndoorCrowd is presented. The model aims at capturing temporal dependencies and the semantic pattern in crowd flow to generate an accurate multi-horizon prediction. With a multi-term temporal dependency capturing structure, the model is effective in learning both daily and weekly patterns of the indoor crowd flow in a shopping mall and is able to provide the temporal interpretation of the prediction result. Moreover, a semantic-temporal fusion module is introduced to utilize the semantic information of stores in prediction, which has proved to be effective in enhancing the model's ability to learn temporal patterns. Experiments were conducted on a real-world dataset to verify the proposed approach. The ablation study demonstrates that the DeepIndoorCrowd can effectively improve the efficiency and accuracy of the prediction up to 18.7%. In addition, some interesting indoor crowd flow patterns were discovered by analyzing the model's interpretation of the prediction result. The proposed prediction method provides an intuitive way of modeling indoor crowd flow, and the experiment's outcome can help indoor managers better understand stores' flow traffic.
学科主题Geography
WOS关键词MODEL
WOS研究方向Geography
出版者WILEY
WOS记录号WOS:001063924500001
源URL[http://ir.igsnrr.ac.cn/handle/311030/197878]  
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, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Chu, Chen,Zhang, Hengcai,Wang, Peixiao,et al. DeepIndoorCrowd: Predicting crowd flow in indoor shopping malls with an interpretable transformer network[J]. TRANSACTIONS IN GIS,2023,N/A.
APA Chu, Chen,Zhang, Hengcai,Wang, Peixiao,&Lu, Feng.(2023).DeepIndoorCrowd: Predicting crowd flow in indoor shopping malls with an interpretable transformer network.TRANSACTIONS IN GIS,N/A.
MLA Chu, Chen,et al."DeepIndoorCrowd: Predicting crowd flow in indoor shopping malls with an interpretable transformer network".TRANSACTIONS IN GIS N/A(2023).

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。