{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T04:27:04Z","timestamp":1767155224081,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger\u2019s life and money. For this purpose, this paper proposes a novel time-based system, namely FraudMove, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed FraudMove system computes the time of the most probable path of a trip. For trajectory outlier detection, a trajectory is considered an outlier trajectory if its time exceeds the time of this computed path by a specified threshold. FraudMove employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows FraudMove to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, FraudMove discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of FraudMove in detecting outlier trajectories. The experimental results prove that FraudMove saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems.<\/jats:p>","DOI":"10.3390\/ijgi10110767","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:02:41Z","timestamp":1636671761000},"page":"767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection"],"prefix":"10.3390","volume":"10","author":[{"given":"Eman O.","family":"Eldawy","sequence":"first","affiliation":[{"name":"Faculty of Computers and Information, Minia University, Minia 61511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3385-3379","authenticated-orcid":false,"given":"Abdeltawab","family":"Hendawi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Statistics, University of Rhode Island, Kingston, NY 02881, USA"},{"name":"Faculty of Computers and Artificial Intelligence, Cairo University, Cairo 11311, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8582-8867","authenticated-orcid":false,"given":"Mohammed","family":"Abdalla","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Beni-Suef University, Giza 8655, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoda M. O.","family":"Mokhtar","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Cairo University, Cairo 11311, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,11]]},"reference":[{"key":"ref_1","unstructured":"Business of Apps (2021, September 01). Uber Revenue and Usage Statistics; Business of Apps. Available online: https:\/\/www.businessofapps.com\/data\/uber-statistics\/."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., and Huang, Y. (2010, January 2\u20135). T-drive: Driving directions based on taxi trajectories. Proceedings of the 18th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869807"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, L., Xie, X., and Ma, W.Y. 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