A gravity-inspired model integrating geospatial and socioeconomic distances for truck origin-destination flows prediction
文献类型：期刊论文
| 作者 | Zhao, Yibo1,2,3; Cheng, Shifen1,2; Gao, Song3; Lu, Feng1,2,4,5 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2025-02-01 |
| 卷号 | 136页码:104328 |
| 关键词 | Heavy trucks Origin-destination flows Gravity-inspired model Spatial interaction Socioeconomic distance Geospatial distance |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2024.104328 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurately predicting truck origin-destination (OD) flows is essential for optimizing logistics systems and promoting coordinated regional development. Existing methods typically assume a monotonic decrease in truck OD flows with increasing geospatial distance, which oversimplifies the complex non-monotonic distribution patterns observed in practice. Moreover, these methods overlook interregional socioeconomic distances and their interaction with geospatial distances, thereby limiting the prediction accuracy and reliability. This study introduces a gravity-inspired model that integrates both geospatial and socioeconomic distances (GSD-DG) to explicitly represent their combined influence on truck OD flows. Specifically, we 1) develop a geospatial distance relation graph using the Weibull function to model the complex spatial distribution patterns of truck OD flows with varying geospatial distances; 2) propose a gravity-inspired representation learning method based on graph attention mechanism to quantify the influence of socioeconomic distance on truck OD flows; and 3) construct a deep gravity model that integrates these distances and their interactions to capture their non-linear relationship with truck OD flows. Extensive experiments on four datasets with varying spatial scale and economic development levels demonstrate that the GSD-DG model improves the robustness and prediction accuracy across diverse spatial distribution patterns, reducing RMSE by 14.2%-85.8% and MSE by 23.5%-92.5% compared to the six baseline models. Incorporating socioeconomic distance and its interaction with geospatial distance further reduces RMSE by 8.5%-36.0%. Additionally, explainable artificial intelligence techniques highlight how these distances affect truck OD flows, providing valuable policy insights for logistics planning and coordinated regional development. |
| URL标识 | 查看原文 |
| WOS关键词 | MOBILITY ; PATTERNS ; LAWS |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001606062300001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219507]  |
| 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.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.Univ Wisconsin Madison, Dept Geog, Geospatial Data Sci Lab, Madison, WI 53706 USA; 4.Fuzhou Univ, Acad Digital China, Fuzhou 350002, Peoples R China; 5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Yibo,Cheng, Shifen,Gao, Song,et al. A gravity-inspired model integrating geospatial and socioeconomic distances for truck origin-destination flows prediction[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2025,136:104328. |
| APA | Zhao, Yibo,Cheng, Shifen,Gao, Song,&Lu, Feng.(2025).A gravity-inspired model integrating geospatial and socioeconomic distances for truck origin-destination flows prediction.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,136,104328. |
| MLA | Zhao, Yibo,et al."A gravity-inspired model integrating geospatial and socioeconomic distances for truck origin-destination flows prediction".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 136(2025):104328. |
入库方式： OAI收割
来源：地理科学与资源研究所
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