{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T12:48:11Z","timestamp":1767703691108,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,10,30]],"date-time":"2017-10-30T00:00:00Z","timestamp":1509321600000},"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>The processing and analysis of trajectories are the core of many location-based applications and services, while trajectory similarity is an essential concept regularly used. To address the time-consuming problem of similarity query, an efficient algorithm based on Fr\u00e9chet distance called Ordered Coverage Judge (OCJ) is proposed, which could realize the filtering query with a given Fr\u00e9chet distance threshold on large-scale trajectory datasets. The OCJ algorithm can obtain the result set quickly by a two-step operation containing morphological characteristic filtering and ordered coverage judgment. The algorithm is expedient to be implemented in parallel for further increases of speed. Demonstrated by experiments over real trajectory data in a multi-core hardware environment, the new algorithm shows favorable stability and scalability besides its higher efficiency in comparison with traditional serial algorithms and other Fr\u00e9chet distance algorithms.<\/jats:p>","DOI":"10.3390\/ijgi6110326","type":"journal-article","created":{"date-parts":[[2017,10,30]],"date-time":"2017-10-30T12:16:23Z","timestamp":1509365783000},"page":"326","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Efficient Query Algorithm for Trajectory Similarity Based on Fr\u00e9chet Distance Threshold"],"prefix":"10.3390","volume":"6","author":[{"given":"Ning","family":"Guo","sequence":"first","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyu","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xiong","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2665-6086","authenticated-orcid":false,"given":"Luo","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Jing","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,30]]},"reference":[{"key":"ref_1","first-page":"108","article-title":"Mining frequent trajectory patterns of moving objects from surveillance video","volume":"28","author":"Dai","year":"2006","journal-title":"J. Natl. Univ. Def. Technol."},{"key":"ref_2","first-page":"103","article-title":"Aggregation analysis of surveillance trajectory based on motion similarity","volume":"35","author":"Liang","year":"2011","journal-title":"J. Natl. Univ. Def. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gowanlock, M., and Casanova, H. (2014). Distance threshold similarity searches on spatiotemporal trajectories using GPGPU. High Perform. Comput., 1\u201310.","DOI":"10.1109\/HiPC.2014.7116913"},{"key":"ref_4","first-page":"43","article-title":"Trajectory Similarity Measures","volume":"7","author":"Kevin","year":"2015","journal-title":"Sigspat. Spec."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Magdy, N., Sakr, M.A., Mostafa, T., and El-Bahnasy, K. (2015, January 12\u201314). Review on trajectory similarity measures. Proceedings of the IEEE Seventh International Conference on Intelligent Computing and Information Systems, Cairo, Egypt.","DOI":"10.1109\/IntelCIS.2015.7397286"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, P., Xu, K., Li, G., and Wan, J. (2016, January 10\u201311). A segmented template optimization using the fr\u00e9chet distance. Proceedings of the International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2016.1102"},{"key":"ref_7","unstructured":"Godau, M. (1991, January 14\u201316). A natural metric for curves\u2014Computing the distance for polygonal chains and approximation algorithms. Proceedings of the Symposium on Theoretical Aspects of Computer Science, Hamburg, Germany."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1142\/S0218195995000064","article-title":"Computing the fr\u00e9chet distance between two polygonal curves","volume":"5","author":"Alt","year":"1995","journal-title":"Int. J. Comput. Geom. Appl."},{"key":"ref_9","unstructured":"Rote, G. (2017, September 11). Free-Space-Diagram. Available online: https:\/\/en.wikipedia.org\/wiki\/Fr\u00e9chet_distance#\/media\/File:Free-space-diagram.png."},{"key":"ref_10","first-page":"636","article-title":"Computing discrete fr\u00e9chet distance","volume":"64","author":"Eiter","year":"1994","journal-title":"See Also"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Agarwal, P.K., Avraham, R.B., Kaplan, H., and Sharir, M. (2013, January 6\u20138). Computing the discrete fr\u00e9chet distance in subquadratic time. Proceedings of the Twenty-Fourth Acm-Siam Symposium on Discrete Algorithms, New Orleans, LA, USA.","DOI":"10.1137\/1.9781611973105.12"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Aronov, B., Har-Peled, S., Knauer, C., Wang, Y., and Wenk, C. (2006, January 11\u201313). Fr\u00e9chet distance for curves, revisited. Proceedings of the European Symposium on Algorithms, Zurich, Switzerland.","DOI":"10.1007\/11841036_8"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1007\/s00454-012-9402-z","article-title":"Approximating the fr\u00e9chet distance for realistic curves in near linear time","volume":"48","author":"Driemel","year":"2012","journal-title":"Discret. Comput. Geom."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Har-Peled, S., Nayyeri, A., and Salavatipour, M. (2012, January 17\u201320). How to walk your dog in the mountains with no magic leash. Proceedings of the Symposium on Computational Geometry, Chapei Hill, NC, USA.","DOI":"10.1145\/2261250.2261269"},{"key":"ref_15","unstructured":"Yoon, S., Yoo, H.M., Yang, S.H., and Park, D.S. (2010, January 3\u20135). Computation of discrete fr\u00e9chet distance using CNN. Proceedings of the International Workshop on Cellular Nanoscale Networks and Their Applications, Berkeley, CA, USA."},{"key":"ref_16","unstructured":"Kevin, B., Maike, B., Wouter, M., and Wolfgang, M. (2014, January 5\u20137). Four soviets walk the dog: With an application to alt\u2019s conjecture. Proceedings of the Symposium on Discrete Algorithms, Portland, OR, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gheibi, A., Maheshwari, A., and Scheffer, C. (2014). Minimum backward fr\u00e9chet distance. Adv. Geogr. Inf. Syst., 381\u2013388.","DOI":"10.1145\/2666310.2666418"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bringmann, K. (2014). Why walking the dog takes time: Fr\u00e9chet distance has no strongly subquadratic algorithms unless SETH fails. Found. Comput. Sci., 661\u2013670.","DOI":"10.1109\/FOCS.2014.76"},{"key":"ref_19","first-page":"227","article-title":"Curve similarity judgement based on the discrete Fr\u00e9chet distance","volume":"55","author":"Zhu","year":"2009","journal-title":"J. Wuhan Univ. (Nat. Sci. 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