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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, Mingxiao4,5,6,7; Gao, Song5; Qiu, Peiyuan3,4; Tu, Wei6,7; Lu, Feng1,2,4; Zhao, Tianhong6,7; Li, Qingquan6,7
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
DOI10.1016/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.Fuzhou Univ, Acad Digital China, Fuzhou 350002, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Shandong Jianzhu Univ, Coll Surveying & Geoinformat, Jinan 250101, 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.Univ Wisconsin, Geospatial Data Sci Lab, Dept Geog, Madison, WI 53706 USA
6.Shenzhen Univ, Res Inst Smart Cities, Sch Architeture & Urban Planning, Shenzhen 518060, Peoples R China
7.Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen Key Lab Spatial Informat Smart Sensing &, 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

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