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Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning

1
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China
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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100049, China
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Mobility Asia, Volkswagen, Beijing 100049, China
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School of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China
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Graduate School of Engineering, Osaka City University, Osaka 5588585, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(5), 333; https://doi.org/10.3390/ijgi10050333
Received: 24 April 2021 / Accepted: 11 May 2021 / Published: 14 May 2021
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. View Full-Text
Keywords: vehicle GNSS trajectory; tracking link; outlier; logistic regression; spatial reasoning vehicle GNSS trajectory; tracking link; outlier; logistic regression; spatial reasoning
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MDPI and ACS Style

Liu, J.; Pan, M.; Song, X.; Wang, J.; Zhu, K.; Li, R.; Rui, X.; Wang, W.; Hu, J.; Raghavan, V. Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning. ISPRS Int. J. Geo-Inf. 2021, 10, 333. https://doi.org/10.3390/ijgi10050333

AMA Style

Liu J, Pan M, Song X, Wang J, Zhu K, Li R, Rui X, Wang W, Hu J, Raghavan V. Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning. ISPRS International Journal of Geo-Information. 2021; 10(5):333. https://doi.org/10.3390/ijgi10050333

Chicago/Turabian Style

Liu, Junli, Miaomiao Pan, Xianfeng Song, Jing Wang, Kemin Zhu, Runkui Li, Xiaoping Rui, Weifeng Wang, Jinghao Hu, and Venkatesh Raghavan. 2021. "Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning" ISPRS International Journal of Geo-Information 10, no. 5: 333. https://doi.org/10.3390/ijgi10050333

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