{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T15:10:00Z","timestamp":1760800200283,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T00:00:00Z","timestamp":1557187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation","award":["61671332, 61671336, U1736206"],"award-info":[{"award-number":["61671332, 61671336, U1736206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification.<\/jats:p>","DOI":"10.3390\/s19092102","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"2102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["User Identification across Asynchronous Mobility Trajectories"],"prefix":"10.3390","volume":"19","author":[{"given":"Mengjun","family":"Qi","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China"},{"name":"Shenzhen Research Institute of Wuhan University, Shenzhen 518057, China"}]},{"given":"Zhongyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China"},{"name":"Shenzhen Research Institute of Wuhan University, Shenzhen 518057, China"}]},{"given":"Zheng","family":"He","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China"},{"name":"Shenzhen Research Institute of Wuhan University, Shenzhen 518057, China"}]},{"given":"Zhenfeng","family":"Shao","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17342","DOI":"10.1109\/ACCESS.2017.2744646","article-title":"User Identification based on Display Names across Online Social Networks","volume":"5","author":"Li","year":"2017","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1177\/0263775815608851","article-title":"No Place to Hide? 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