{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:48:55Z","timestamp":1760147335652,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702010","61972439","2208085MF164","KJ2021A0125","KJ2020ZD61"],"award-info":[{"award-number":["61702010","61972439","2208085MF164","KJ2021A0125","KJ2020ZD61"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui Provincial Natural Science Foundation of China","award":["61702010","61972439","2208085MF164","KJ2021A0125","KJ2020ZD61"],"award-info":[{"award-number":["61702010","61972439","2208085MF164","KJ2021A0125","KJ2020ZD61"]}]},{"name":"University Natural Science Research Program of Anhui Province","award":["61702010","61972439","2208085MF164","KJ2021A0125","KJ2020ZD61"],"award-info":[{"award-number":["61702010","61972439","2208085MF164","KJ2021A0125","KJ2020ZD61"]}]},{"name":"Major Natural Science Research Projects of Higher Education Institutions in Anhui Province","award":["61702010","61972439","2208085MF164","KJ2021A0125","KJ2020ZD61"],"award-info":[{"award-number":["61702010","61972439","2208085MF164","KJ2021A0125","KJ2020ZD61"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Abnormal-trajectory detection can be used to detect fraudulent behavior by taxi drivers when carrying passengers. Existing methods usually detect abnormal trajectories based on the characteristics of \u201cfew and different\u201d, which require large data sets and, therefore, may identify \u201cfew and near\u201d trajectories chosen by drivers according to their driving experience as abnormal situations. This study proposed an abnormal-trajectory detection method based on a variable grid to address this problem. First, the urban road network was divided into three regions: high-, medium-, and low-density road network regions using a kernel density analysis method. Second, grids with different sizes were set for different types of road network regions; trajectory tuples were obtained based on the grid division results, and the abnormality rate of the trajectory was calculated. Finally, a trajectory-abnormality probability function was developed to calculate the deviation of each trajectory from the benchmark trajectory to detect abnormal trajectories. Experimental results on a real taxi trajectory dataset demonstrated that the proposed method achieved a higher accuracy in detecting abnormal trajectories than similar methods.<\/jats:p>","DOI":"10.3390\/ijgi12020040","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:56:48Z","timestamp":1675065408000},"page":"40","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Abnormal-Trajectory Detection Method Based on Variable Grid Partitioning"],"prefix":"10.3390","volume":"12","author":[{"given":"Chuanming","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, School of Computer and Information, Anhui Normal University, Wuhu 241002, China"}]},{"given":"Dongsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, School of Computer and Information, Anhui Normal University, Wuhu 241002, China"}]},{"given":"Qingying","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, School of Computer and Information, Anhui Normal University, Wuhu 241002, China"}]},{"given":"Shan","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, School of Computer and Information, Anhui Normal University, Wuhu 241002, China"}]},{"given":"Gege","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, School of Computer and Information, Anhui Normal University, Wuhu 241002, China"}]},{"given":"Haoming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Normal University, Wuhu 241002, China"},{"name":"Anhui Provincial Key Laboratory of Network and Information Security, School of Computer and Information, Anhui Normal University, Wuhu 241002, China"}]},{"given":"Wen","family":"Chen","sequence":"additional","affiliation":[{"name":"Anhui Provincial Key Laboratory of Network and Information Security, School of Computer and Information, Anhui Normal University, Wuhu 241002, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","first-page":"100304","article-title":"Detection of anomalous vehicles using physics of traffic","volume":"27","author":"Ranaweera","year":"2021","journal-title":"Veh. 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