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Predicting origin-destination flows by considering heterogeneous mobility patterns

文献类型：期刊论文

作者Zhao, Yibo2,3,4; Cheng, Shifen3,4; Gao, Song2; Wang, Peixiao3,4; Lu, Feng1,3,4,5
刊名SUSTAINABLE CITIES AND SOCIETY
出版日期2025
卷号118页码:106015
关键词Origin-destination flow Spatial interaction Urban mobility Spatial heterogeneity Imbalanced data learning Graph attention network
ISSN号2210-6707
DOI10.1016/j.scs.2024.106015
产权排序1
文献子类Article
英文摘要The accurate prediction of origin-destination (OD) flows is essential for advancing sustainable urban mobility and supporting resilient urban planning. However, the inherent heterogeneity of mobility patterns results in complex geographic unit relations, diverse spatial organizational structures, and the long-tailed effect on OD flow distribution. This study proposes a novel OD flow prediction method based on graph-based deep learning (named as HMCG-LGBM). Specifically, 1) a modularity-based graph reconstruction strategy is presented for geographic unit relation augmentation by eliminating weak connections; 2) the heterogeneous spatial organization of OD flows is captured by combining the community detection approach and graph attention mechanism with the introduction of socio-economic and spatial features; and 3) a weighted loss function with distribution smoothing paradigm is developed to enhance the prediction for low-probability mobility events, addressing the challenges posed by long-tailed distributions. Extensive experiments conducted on real-world datasets show that the predictive performance of the proposed method is significantly improved, with the RMSE and MAE reduced from the baselines by 11.1%-33.3% and 14.1%-22.2%, respectively. The results also demonstrate the robustness of the proposed method for dealing with imbalanced OD flow distributions, providing valuable insights for spatial interaction predictive modeling in the context of sustainable urban systems.
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WOS研究方向Construction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels
语种英语
WOS记录号WOS:001373086600001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/211374]  
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Cheng, Shifen
作者单位1.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
2.Univ Wisconsin, Dept Geog, Geospatial Data Sci Lab, Madison, WI 53706 USA;
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
5.Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China;
推荐引用方式
GB/T 7714		 Zhao, Yibo,Cheng, Shifen,Gao, Song,et al. Predicting origin-destination flows by considering heterogeneous mobility patterns[J]. SUSTAINABLE CITIES AND SOCIETY,2025,118:106015.
APA Zhao, Yibo,Cheng, Shifen,Gao, Song,Wang, Peixiao,&Lu, Feng.(2025).Predicting origin-destination flows by considering heterogeneous mobility patterns.SUSTAINABLE CITIES AND SOCIETY,118,106015.
MLA Zhao, Yibo,et al."Predicting origin-destination flows by considering heterogeneous mobility patterns".SUSTAINABLE CITIES AND SOCIETY 118(2025):106015.

入库方式： OAI收割

来源：地理科学与资源研究所

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