Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning
Bibliographic Data
| ID | 22032335 |
|---|---|
| Authors | Junli Liu (0000-0001-6119-0301, University of Chinese Academy of Sciences), Miaomiao Pan (Chinese Academy of Sciences), Xianfeng Song (0000-0001-6871-8199, Chinese Academy of Sciences, corresponding author), Jing Wang (0000-0002-7594-8539, Mobility Asia, Volkswagen, Beijing 100049, China), Kemin Zhu (0000-0003-1603-9869, University of Chinese Academy of Sciences), Runkui Li (0000-0002-5822-5072, Chinese Academy of Sciences), Xiaoping Rui (0000-0002-7764-4272, Hohai University), Weifeng Wang (0000-0002-9752-6185, University of Chinese Academy of Sciences), Jinghao Hu (University of Chinese Academy of Sciences), Venkatesh Raghavan (0000-0003-1908-5940, Osaka City University) |
| Year | 2021 |
| Volume | 10 |
| Issue | 5 |
| Pages | 333 |
| Publication date | 2021-05-14 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10050333 |
| OpenAlex | W3197080772 |
| Language | EN |
| References cited | 34 |
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
Anomaly detection · Computer vision · Data mining · Global Positioning System · GNSS applications · Outlier · Trajectory · Automated Road and Building Extraction · Computer Science · Data Management and Algorithms · Mathematics · Transportation Planning and Optimization · Artificial Intelligence
| Highly cited | No |
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