Fine-grained crowd distribution forecasting with multi-order spatial interactions using mobile phone data
文献类型:期刊论文
作者 | Li, Mingxiao1,5,6,7; Gao, Song6; Qiu, Peiyuan4,5; Tu, Wei1,7; Lu, Feng2,3,5; Zhao, Tianhong1,7; Li, Qingquan1,7 |
刊名 | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES |
出版日期 | 2022-11-01 |
卷号 | 144页码:18 |
ISSN号 | 0968-090X |
关键词 | Crowd distribution forecasting Multi-order spatial interaction Embedding learning Trajectory enhancement Human mobility |
DOI | 10.1016/j.trc.2022.103908 |
通讯作者 | Gao, Song(song.gao@wisc.edu) |
英文摘要 | Fine-grained crowd distribution forecasting benefits smart transportation operations and management, such as public transport dispatch, traffic demand prediction, and transport emergency response. Considering the co-evolutionary patterns of crowd distribution, the interactions among places are essential for modelling crowd distribution variations. However, two issues remain. First, the lack of sampling design in passive big data acquisition makes the spatial interaction characterizations of less crowded places insufficient. Second, the multi-order spatial interactions among places can help forecasting crowd distribution but are rarely considered in the existing literature. To address these issues, a novel crowd distribution forecasting method with multiorder spatial interactions was proposed. In particular, a weighted random walk algorithm was applied to generate simulated trajectories for improving the interaction characterizations derived from sparse mobile phone data. The multi-order spatial interactions among contextual nonadjacent places were modelled with an embedding learning technique. The future crowd distribution was forecasted via a graph-based deep neural network. The proposed method was verified using a real-world mobile phone dataset, and the results showed that both the multi-order spatial interactions and the trajectory data enhancement algorithm helped improve the crowd distribution forecasting performance. The proposed method can be utilized for capturing fine-grained crowd distribution, which supports various applications such as intelligent transportation management and public health decision making. |
WOS关键词 | TRAFFIC FLOW ; INTERACTION PATTERNS ; POPULATION ; MODEL ; PREDICTION ; TRAJECTORIES ; INFORMATION ; MOVEMENTS |
资助项目 | National Natural Science Foundation of China[42101463] ; National Natural Science Foundation of China[42071360] ; National Natural Science Foundation of China[42271474] ; National Natural Science Foundation of China[71961137003] ; China Postdoctoral Science Foundation[2021 M692164] ; Guangdong Province Basic and Applied Basic Research Fund[2020A1515111166] ; State Key Laboratory of Resources and Environmental Information System |
WOS研究方向 | Transportation |
语种 | 英语 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
WOS记录号 | WOS:000874665700004 |
资助机构 | National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Guangdong Province Basic and Applied Basic Research Fund ; State Key Laboratory of Resources and Environmental Information System |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/186294] |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Gao, Song |
作者单位 | 1.Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen Key Lab Spatial Informat Smart Sensing &, Shenzhen 518060, Peoples R China 2.Fuzhou Univ, Acad Digital China, Fuzhou 350002, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Shandong Jianzhu Univ, Coll Surveying & Geoinformat, Jinan 250101, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 6.Univ Wisconsin, Geospatial Data Sci Lab, Dept Geog, Madison, WI 53706 USA 7.Shenzhen Univ, Res Inst Smart Cities, Sch Architeture & Urban Planning, Shenzhen 518060, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Mingxiao,Gao, Song,Qiu, Peiyuan,et al. Fine-grained crowd distribution forecasting with multi-order spatial interactions using mobile phone data[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,2022,144:18. |
APA | Li, Mingxiao.,Gao, Song.,Qiu, Peiyuan.,Tu, Wei.,Lu, Feng.,...&Li, Qingquan.(2022).Fine-grained crowd distribution forecasting with multi-order spatial interactions using mobile phone data.TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,144,18. |
MLA | Li, Mingxiao,et al."Fine-grained crowd distribution forecasting with multi-order spatial interactions using mobile phone data".TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 144(2022):18. |
入库方式: OAI收割
来源:地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。