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Predicting human-activity intensity in urban areas with a prior-enhanced probabilistic-deterministic model

文献类型：期刊论文

作者Zhong, Cheng3; Wu, Sheng3; Wang, Peixiao1,2; Zhang, Hengcai1,2; Cheng, Shifen1,2; Lu, Feng1,2
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2025-09-22
卷号N/A
关键词Prior knowledge dual-mode human activity intensity probabilistic prediction deterministic prediction
ISSN号1365-8816
DOI10.1080/13658816.2025.2562250
产权排序2
文献子类Article ; Early Access
英文摘要Although numerous models have been proposed to predict the intensity of human activities in urban areas, two major issues hamper the performance of existing models: (1) fail to incorporate appropriate prior knowledge instrumental for improving accuracy and interpretability; (2) fail to integrate probabilistic and deterministic predictions to achieve complementary strengths, namely uncertainty quantification and high predictive accuracy. To address these challenges, we proposed a prior-enhanced dual-mode spatiotemporal graph neural network (PED-STGNN) to support both probabilistic and deterministic predictions. Specifically, we introduced a hypergraph node-to-vector (hypernode2vec) method to capture the multivariate functional similarity prior derived from complex and multivariate relations between urban regions. This functional similarity characterizes urban systems more precisely than existing methods relying on first-order pairwise relations. It improves accuracy and interpretability while enabling spatial modeling of higher-order multivariate relations beyond first-order pairwise relations. We also designed a plug-and-play probabilistic prediction module that enables switches between probabilistic and deterministic modes. Experiments based on the human activity intensity in Fuzhou, China, demonstrated the advantages in accuracy, interpretability and multi-scenario applicability.
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WOS关键词CONVOLUTIONAL NETWORK ; PASSENGER FLOW
WOS研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
语种英语
WOS记录号WOS:001577091200001
出版者TAYLOR & FRANCIS LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/216119]  
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Wang, Peixiao
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 Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
3.Fuzhou Univ, Acad Digital China Fujian, Fuzhou, Peoples R China;
推荐引用方式
GB/T 7714		 Zhong, Cheng,Wu, Sheng,Wang, Peixiao,et al. Predicting human-activity intensity in urban areas with a prior-enhanced probabilistic-deterministic model[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2025,N/A.
APA Zhong, Cheng,Wu, Sheng,Wang, Peixiao,Zhang, Hengcai,Cheng, Shifen,&Lu, Feng.(2025).Predicting human-activity intensity in urban areas with a prior-enhanced probabilistic-deterministic model.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A.
MLA Zhong, Cheng,et al."Predicting human-activity intensity in urban areas with a prior-enhanced probabilistic-deterministic model".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2025).

入库方式： OAI收割

来源：地理科学与资源研究所

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