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Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning

文献类型：期刊论文

作者Liu, Junli2; Pan, Miaomiao3; Song, Xianfeng2,3,4; Wang, Jing5; Zhu, Kemin2; Li, Runkui2,4; Rui, Xiaoping6; Wang, Weifeng2; Hu, Jinghao2; Raghavan, Venkatesh1
刊名ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版日期2021-05-01
卷号10期号:5页码:19
关键词vehicle GNSS trajectory tracking link outlier logistic regression spatial reasoning
DOI10.3390/ijgi10050333
通讯作者Song, Xianfeng(xfsong@ucas.ac.cn)
英文摘要Vehicle trajectories derived from Global Navigation Satellite Systems (GNSS) are used in various traffic applications based on trajectory quality analysis for the development of successful traffic models. A trajectory consists of points and links that are connected, where both the points and links are subject to positioning errors in the GNSS. Existing trajectory filters focus on point outliers, but neglect link outliers on tracks caused by a long sampling interval. In this study, four categories of link outliers are defined, i.e., radial, drift, clustered, and shortcut; current available algorithms are applied to filter apparent point outliers for the first three categories, and a novel filtering approach is proposed for link outliers of the fourth category in urban areas using spatial reasoning rules without ancillary data. The proposed approach first measures specific geometric properties of links from trajectory databases and then evaluates the similarities of geometric measures among the links, following a set of spatial reasoning rules to determine link outliers. We tested this approach using taxi trajectory datasets for Beijing with a built-in sampling interval of 50 to 65 s. The results show that clustered links (27.14%) account for the majority of link outliers, followed by shortcut (6.53%), radial (3.91%), and drift (0.62%) outliers.
WOS关键词URBAN ; INTERSECTION ; INFERENCE
资助项目National Key Research and Development Program of China[2017YFB0503702] ; National Key Research and Development Program of China[2017YFB0503605] ; National Key Research and Development Program of China[2016YFC0503602] ; National Key Research and Development Program of China[2016YFB0501805] ; National Natural Science Foundation of China[40771167] ; National Natural Science Foundation of China[41771435] ; National Natural Science Foundation of China[41201038] ; National Natural Science Foundation of China[41601486] ; China Scholarship Council[201704910297] ; Guangxi Science and Technology Major Project[GK-AA17202033]
WOS研究方向Computer Science ; Physical Geography ; Remote Sensing
语种英语
出版者MDPI
WOS记录号WOS:000653975600001
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; China Scholarship Council ; Guangxi Science and Technology Major Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/162480]  
专题中国科学院地理科学与资源研究所
通讯作者Song, Xianfeng
作者单位1.Osaka City Univ, Grad Sch Engn, Osaka 5588585, Japan
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Quantitat Remote Sensing Informat Technol, Beijing 100094, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100049, Peoples R China
5.Volkswagen, Mobil Asia, Beijing 100049, Peoples R China
6.Hohai Univ, Sch Earth Sci & Engn, Nanjing 211000, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Junli,Pan, Miaomiao,Song, Xianfeng,et al. Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2021,10(5):19.
APA Liu, Junli.,Pan, Miaomiao.,Song, Xianfeng.,Wang, Jing.,Zhu, Kemin.,...&Raghavan, Venkatesh.(2021).Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,10(5),19.
MLA Liu, Junli,et al."Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 10.5(2021):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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