{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:06:01Z","timestamp":1769558761226,"version":"3.49.0"},"reference-count":22,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Hunan Provincial Natural Science Foundation of China","award":["2019JJ10004"],"award-info":[{"award-number":["2019JJ10004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Positioning devices allow users\u2019 movement to be recorded. The GPS (Global Positioning System) trajectory data typically consists of spatiotemporal points, which make up the major part of the big data concerning urban life. Existing knowledge extraction methods about the trajectory share a general limitation\u2014they only investigate data from a spatiotemporal aspect, but fail to take the semantic information of trajectories into consideration. Therefore, extracting the semantic information of trajectories with the context of big data is challenging pattern recognition task that has practical application prospects. In this paper, a system is proposed to extract the semantic trajectory patterns of positioning device users. Firstly, a spatiotemporal threshold and clustering based pre-processing model is proposed to process the raw data. Then, we design a probabilistic generative model to annotate the semantic information of each trajectory after the pre-processing procedure. Finally, we apply the PrefixSpan algorithm to mine the semantic trajectory patterns. We verify our system on a large dataset of users\u2019 real trajectories over a period of 5 years in Beijing, China. The results of the experiment indicate that our system produces meaningful patterns.<\/jats:p>","DOI":"10.3390\/sym11070889","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T11:02:37Z","timestamp":1562583757000},"page":"889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A System of Mining Semantic Trajectory Patterns from GPS Data of Real Users"],"prefix":"10.3390","volume":"11","author":[{"given":"Wanlong","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electronic and Science, National University of Defense Technology, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic and Science, National University of Defense Technology, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhitao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electronic and Science, National University of Defense Technology, Changsha 410000, China"},{"name":"Academic Research Office, National University of Defense Technology, Changsha 410000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., and Xie, X. 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