Inferring freeway traffic volume with spatial interaction enhanced betweenness centrality
文献类型：期刊论文
| 作者 | Zhang, Beibei3,4; Cheng, Shifen3,4; Wang, Peixiao3,4; Lu, Feng1,2,3,4 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 129页码:103818 |
| 关键词 | Freeway traffic inference Spatial interaction Economic development indicator Betweenness centrality |
| DOI | 10.1016/j.jag.2024.103818 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Freeway traffic volume is strongly correlated with the intensity of regional socioeconomic spatial interactions and the road network structure. Although existing studies have proposed indicators of betweenness centrality (BC) integrated into regional spatial interactions, the socio-economic drivers of freeway traffic volume formation have been neglected. More importantly, existing studies have not established a non-linear response relationship among BC, city socio-economic spatial interactions, and road traffic volume, which severely limits the comprehensive quantification of the role of freeway traffic flow drivers. Therefore, this study proposes a freeway traffic volume inference method that integrates spatial interaction to enhance BC. First, the socioeconomic factors of the origin and destination cities are incorporated into the BC indicator to create an enhanced betweenness centrality indicator (ODBC), which quantifies the strength of spatial interactions between cities. Second, a machine learning approach is used to develop the non-linear response relationship between ODBC and freeway traffic flow to accurately infer traffic volume. Finally, utilizing the SHapley additive explanation approach, the role vectors of intercity freeway traffic volume drivers are quantified. Experiments conducted on data from freeway toll stations demonstrate that the proposed method surpasses the baseline method based on BC and weighted by BC considering only the potential destination or origin city attractiveness, with an improvement in R2 of 14%, 4.2%, and 4%, and a maximum reduction in RMSE of 40%, 24.5%, and 26%. The proposed method yields higher accuracy for unknown road segments and is easily interpretable. |
| WOS关键词 | MODEL |
| WOS研究方向 | Remote Sensing |
| WOS记录号 | WOS:001226892700001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205171]  |
| 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 Resource Dev & Applicat, Nanjing 210023, Peoples R China 2.Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, IGSNRR, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Beibei,Cheng, Shifen,Wang, Peixiao,et al. Inferring freeway traffic volume with spatial interaction enhanced betweenness centrality[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,129:103818. |
| APA | Zhang, Beibei,Cheng, Shifen,Wang, Peixiao,&Lu, Feng.(2024).Inferring freeway traffic volume with spatial interaction enhanced betweenness centrality.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,129,103818. |
| MLA | Zhang, Beibei,et al."Inferring freeway traffic volume with spatial interaction enhanced betweenness centrality".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 129(2024):103818. |
入库方式： OAI收割
来源：地理科学与资源研究所
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