{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T13:45:20Z","timestamp":1783691120347,"version":"3.55.0"},"reference-count":80,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,4]],"date-time":"2018-01-04T00:00:00Z","timestamp":1515024000000},"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>Large volumes of trajectory-based data require development of appropriate data manipulation mechanisms that will offer efficient computational solutions. In particular, identification of meaningful geometric points of such trajectories is still an open research issue. Detection of these critical points implies to identify self-intersecting, turning and curvature points so that specific geometric characteristics that are worth identifying could be denoted. This research introduces an approach called Trajectory Critical Point detection using Convex Hull (TCP-CH) to identify a minimum number of critical points. The results can be applied to large trajectory data sets in order to reduce storage costs and complexity for further data mining and analysis. The main principles of the TCP-CH algorithm include computing: convex areas, convex hull curvatures, turning points, and intersecting points. The experimental validation applied to Geolife trajectory dataset reveals that the proposed framework can identify most of intersecting points in reasonable computing time. Finally, comparison of the proposed algorithm with other methods, such as turning function shows that our approach performs relatively well when considering the overall detection quality and computing time.<\/jats:p>","DOI":"10.3390\/ijgi7010014","type":"journal-article","created":{"date-parts":[[2018,1,4]],"date-time":"2018-01-04T11:52:47Z","timestamp":1515066767000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls"],"prefix":"10.3390","volume":"7","author":[{"given":"Amin","family":"Hosseinpoor Milaghardan","sequence":"first","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, North Kargar Street, After Ale-Ahmad Junction, 1439957131 Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7133-3844","authenticated-orcid":false,"given":"Rahim","family":"Ali Abbaspour","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, North Kargar Street, After Ale-Ahmad Junction, 1439957131 Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5586-1997","authenticated-orcid":false,"given":"Christophe","family":"Claramunt","sequence":"additional","affiliation":[{"name":"Naval Academy Research Institute Lanveoc-Poulmic, BP 600, 29240 Brest Naval, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/13658816.2016.1213845","article-title":"A trajectory clustering approach based on decision graph and data field for detecting hotspots","volume":"31","author":"Zhao","year":"2016","journal-title":"Int. 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