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Second, according to the grid density, the grids were divided into high- and low-density grids, and the code sequence for each trajectory was obtained using grid coding and density. Third, the trajectory dataset is divided into several groups based on the number of low-density grids through which each trajectory passes. Finally, based on the high-density grid sequences, a regular subtrajectory dataset was obtained within each trajectory group, which was used to calculate the trajectory deviation to detect outlying trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods.<\/jats:p>","DOI":"10.3233\/ida-237384","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T11:29:29Z","timestamp":1708687769000},"page":"415-432","source":"Crossref","is-referenced-by-count":0,"title":["Trajectory outlier detection method based on group division"],"prefix":"10.1177","volume":"28","author":[{"given":"Chuanming","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, Anhui, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu, Anhui, 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