Predicting urban signal-controlled intersection congestion events using spatio-temporal neural point process
文献类型：期刊论文
| 作者 | Wang, Jianlong5; Duan, Xiaoqi4; Wang, Peixiao3; Qiu, A. -Gen2; Chen, Zeqiang1 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2024-12-31 |
| 卷号 | 17期号:1页码:24 |
| 关键词 | Signal-controlled intersections congestion prediction spatio-temporal dependencies temporal point process congestion events |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2024.2376270 |
| 英文摘要 | The urban traffic signal-controlled intersections are of great significance for solving the problem of urban road congestion. Previous research on congestion prediction mainly aggregated data at the level of road segments or traffic flow at a coarse regulated time interval. Fine-grained prediction of congestion events at the lane-level and cycle-level enables detailed a understanding of spatio-temporal dependencies, leading to congestion reduction, improved efficiency. This paper presents a Spatio-Temporal Neural Point Process (STNPP) model that combines Graph Neural Networks and Neural Temporal Point Process to predict congestion events at urban intersections. The proposed model allows for complete prediction of congestion events, including their occurrence, development, dissipation. In the process of spatial correlation modeling, graph neural networks are used to model the spatial relationships between both region and intersections. The current intersection and its upstream/downstream areas are modeled separately. To model the temporal correlations at individual intersections, we focus on a specific lane and capture the evolution of congestion events using the Neural Point Process Gated Recurrent Unit (NPPGRU), which captures the temporal granularity changes of signal-controlled cycles in congestion events. Using actual traffic speed and signal-controlled data from Hangzhou city, we validate that the proposed method achieves stable predictive performance. |
| WOS关键词 | NETWORKS |
| 资助项目 | National Key Research and Development Program of China[2021YFB3101100] ; Guizhou University talent introduction project[(2022) 49] ; Basic research project of Guizhou University[[2024] 16] ; The 2024 Basic Research Program (Natural Science) Youth Guidance Project, Chinese Academy of Surveying and Mapping Basic Research Fund Program[AR2204] ; Open Fund of National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China[2023KFJJ09] ; China Postdoctoral Science Foundation[2023M743454] |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001281663200001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Key Research and Development Program of China ; Guizhou University talent introduction project ; Basic research project of Guizhou University ; The 2024 Basic Research Program (Natural Science) Youth Guidance Project, Chinese Academy of Surveying and Mapping Basic Research Fund Program ; Open Fund of National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China ; China Postdoctoral Science Foundation |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207330]  |
| 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 | Duan, Xiaoqi |
| 作者单位 | 1.China Univ Geosci, Natl Engn Res Ctr Geog Informat Syst, Wuhan, Peoples R China 2.Chinese Acad Surverying & Mapping, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 4.Guizhou Univ, Coll Comp Sci & Technol, State Key Lab Publ Big Data, Guiyang, Peoples R China 5.Changjiang Spatial Informat Technol Engn Co Ltd, Wuhan 430010, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jianlong,Duan, Xiaoqi,Wang, Peixiao,et al. Predicting urban signal-controlled intersection congestion events using spatio-temporal neural point process[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2024,17(1):24. |
| APA | Wang, Jianlong,Duan, Xiaoqi,Wang, Peixiao,Qiu, A. -Gen,&Chen, Zeqiang.(2024).Predicting urban signal-controlled intersection congestion events using spatio-temporal neural point process.INTERNATIONAL JOURNAL OF DIGITAL EARTH,17(1),24. |
| MLA | Wang, Jianlong,et al."Predicting urban signal-controlled intersection congestion events using spatio-temporal neural point process".INTERNATIONAL JOURNAL OF DIGITAL EARTH 17.1(2024):24. |
入库方式： OAI收割
来源：地理科学与资源研究所
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