{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T13:08:05Z","timestamp":1776258485212,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003566","name":"Ministry of Oceans and Fisheries","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003566","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,13]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>This study presents a novel statistical trajectory-distance metric specialized for nautical route clustering analysis. Based on the dynamic time warping (DTW) metric, one of the most used metrics for trajectory-distance, the statistical trajectory-distance metric was defined by replacing the distance term in DTW with a linear combination of the Jensen\u2013Shannon divergence and Wasserstein distance. Each waypoint from a nautical route was modelled as a discrete and asymmetric binomial normal distribution defined by the cross-track distance (XTD) of the waypoint. The model was then used to compute the statistical distance between waypoints. Nautical route clustering was performed using density-based spatial clustering of applications with noise and the statistical trajectory-distance metric. The nautical route for the clustering analysis, including the XTD information, was extracted from automatic identification system data from the southern sea of the Korean Peninsula. The clustering results were evaluated by comparing them with the results of other popular trajectory-distance metrics. The proposed method was more effective compared to other trajectory-distance when the trajectories pass on both sides of a small island, which is frequent case in coastal route clustering.<\/jats:p>","DOI":"10.1093\/jcde\/qwac024","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T12:12:54Z","timestamp":1647951174000},"page":"731-754","source":"Crossref","is-referenced-by-count":7,"title":["Statistical trajectory-distance metric for nautical route clustering analysis using cross-track distance"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6362-2585","authenticated-orcid":false,"given":"Wonchul","family":"Yoo","sequence":"first","affiliation":[{"name":"Department of Naval Architecture and Ocean Engineering, Research Institute of Marine Systems Engineering, Seoul National University, Seoul 08826, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3385-4657","authenticated-orcid":false,"given":"Tae-wan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Naval Architecture and Ocean Engineering, Research Institute of Marine Systems Engineering, Seoul National University, Seoul 08826, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"2022050612543777600_bib1","first-page":"4851","article-title":"Learning traffic patterns at intersections by spectral clustering of motion trajectories [application of Hausdorff (modified)]","volume-title":"IEEE International Conference on Intelligent Robots and Systems","author":"Atev","year":"2006"},{"key":"2022050612543777600_bib2","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1109\/TITS.2010.2048101","article-title":"Clustering of vehicle trajectories [application of Hausdorff (modified)]","volume":"11","author":"Atev","year":"2010","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"2022050612543777600_bib3","first-page":"623","article-title":"Segmented trajectory-based indexing and retrieval of video data","volume":"2","author":"Bashir","year":"2003","journal-title":"IEEE International Conference on Image Processing"},{"key":"2022050612543777600_bib4","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1109\/TIP.2007.898960","article-title":"Object trajectory-based activity classification and recognition using hidden Markov models","volume":"16","author":"Bashir","year":"2007","journal-title":"IEEE Transactions on Image Processing"},{"key":"2022050612543777600_bib5","first-page":"521","article-title":"Extraction and clustering of motion trajectories in video","volume-title":"Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004)","author":"Buzan","year":"2004"},{"key":"2022050612543777600_bib6","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/ICBDA.2018.8367725","article-title":"PCA-based hierarchical clustering of ais trajectories with automatic extraction of clusters","volume-title":"2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)","author":"Cao","year":"2018"},{"issue":"2","key":"2022050612543777600_bib7","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A cluster separation measure","volume":"PAMI-1","author":"Davies","year":"1979","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2022050612543777600_bib8","volume-title":"Global positioning system standard positioning service performance standard (5th ed.)","author":"Department of Defense,\u00a0United States of America","year":"2020"},{"issue":"2","key":"2022050612543777600_bib9","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3138\/FM57-6770-U75U-7727","article-title":"Algorithms for the reduction of the number of points required to represent a digitized line or its caricature","volume":"10","author":"Douglas","year":"1973","journal-title":"Cartographica: The International Journal for Geographic Information and Geovisualization"},{"key":"2022050612543777600_bib10","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume-title":"KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining","author":"Ester","year":"1996"},{"key":"2022050612543777600_bib11","first-page":"602","article-title":"Similarity based vehicle trajectory clustering and anomaly detection","volume-title":"IEEE International Conference on Image Processing 2005","author":"Fu","year":"2005"},{"key":"2022050612543777600_bib12","author":"GEBCO Compilation Group","year":"2020","journal-title":"GEBCO 2020 grid"},{"key":"2022050612543777600_bib13","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/ITSC.2013.6728277","article-title":"Trajectory clustering based on length scale directive Hausdorff","volume-title":"16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)","author":"Hao","year":"2013"},{"key":"2022050612543777600_bib14","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TIP.2006.891352","article-title":"Semantic-based surveillance video retrieval","volume":"16","author":"Hu","year":"2007","journal-title":"IEEE Transactions on Image Processing"},{"key":"2022050612543777600_bib15","volume-title":"IHO specifications for chart content and display aspects of ECDIS","author":"International Hydrographic Organization","year":"1996"},{"key":"2022050612543777600_bib16","volume-title":"IHO transfer standard for digital hydrographic data","author":"International Hydrographic Organization","year":"2014"},{"key":"2022050612543777600_bib17","volume-title":"S-57 Edition 3.1 Supplement No. 3, IHO transfer standard for digital hydrographic data, Supplementary information for the encoding of S-57 Edition 3.1 ENC Data","author":"International Hydrographic Organization","year":"2014"},{"key":"2022050612543777600_bib18","volume-title":"International convention for the safety of life at sea (SOLAS) Chapter V: Safety of navigation","author":"International Maritime Organization","year":"2002"},{"key":"2022050612543777600_bib19","doi-asserted-by":"crossref","DOI":"10.1145\/2833165.2833173","article-title":"A new trajectory similarity measure for GPS data","volume-title":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS\u201915","author":"Ismail","year":"2015"},{"issue":"23","key":"2022050612543777600_bib20","doi-asserted-by":"crossref","first-page":"9209","DOI":"10.5194\/acp-9-9209-2009","article-title":"A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area","volume":"9","author":"Jalkanen","year":"2009","journal-title":"Atmospheric Chemistry and Physics"},{"key":"2022050612543777600_bib21","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/ICPR.2004.1334359","article-title":"Multi feature path modeling for video surveillance","volume-title":"Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004","author":"Junejo","year":"2004"},{"key":"2022050612543777600_bib22","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.oceaneng.2018.02.060","article-title":"Data-driven based automatic maritime routing from massive ais trajectories in the face of disparity","volume":"155","author":"kai\u00a0Zhang","year":"2018","journal-title":"Ocean Engineering"},{"key":"2022050612543777600_bib23","volume-title":"Finding groups in data: An introduction to cluster analysis","author":"Kaufman","year":"2009"},{"key":"2022050612543777600_bib24","doi-asserted-by":"crossref","first-page":"566","DOI":"10.3390\/jmse8080566","article-title":"Zone of confidence impact on cross track limit determination in ECDIS passage planning","volume":"8","author":"Kristi","year":"2020","journal-title":"Journal of Marine Science and Engineering 2020"},{"issue":"8","key":"2022050612543777600_bib25","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1016\/j.ress.2009.02.028","article-title":"Analysis of the marine traffic safety in the Gulf of Finland","volume":"94","author":"Kujala","year":"2009","journal-title":"Reliability Engineering & System Safety"},{"key":"2022050612543777600_bib26","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1145\/1247480.1247546","article-title":"Trajectory clustering: A partition-and-group framework","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data","author":"Lee","year":"2007"},{"issue":"1","key":"2022050612543777600_bib27","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10115-015-0845-4","article-title":"A framework for anomaly detection in maritime trajectory behavior","volume":"47","author":"Lei","year":"2016","journal-title":"Knowledge and Information Systems"},{"key":"2022050612543777600_bib28","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.3390\/s17081792","article-title":"A dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis","volume":"17","author":"Li","year":"2017","journal-title":"Sensors"},{"key":"2022050612543777600_bib29","doi-asserted-by":"crossref","first-page":"58939","DOI":"10.1109\/ACCESS.2018.2866364","article-title":"Spatio-temporal vessel trajectory clustering based on data mapping and density","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"2022050612543777600_bib30","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.ins.2020.04.009","article-title":"Adaptively constrained dynamic time warping for time series classification and clustering","volume":"534","author":"Li","year":"2020","journal-title":"Information Sciences"},{"key":"2022050612543777600_bib31","doi-asserted-by":"crossref","first-page":"108803","DOI":"10.1016\/j.oceaneng.2021.108803","article-title":"An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation","volume":"225","author":"Liang","year":"2021","journal-title":"Ocean Engineering"},{"key":"2022050612543777600_bib32","first-page":"777","article-title":"Semantic interpretation of object activities in a surveillance system","volume-title":"2002 International Conference on Pattern Recognition","author":"Lou","year":"2002"},{"key":"2022050612543777600_bib33","first-page":"312","article-title":"Learning trajectory patterns by clustering: Experimental studies and comparative evaluation [reviews]","volume-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","author":"Morris","year":"2010"},{"key":"2022050612543777600_bib34","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1109\/TCSVT.2008.927109","article-title":"A survey of vision-based trajectory learning and analysis for surveillance","volume":"18","author":"Morris","year":"2008","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"2022050612543777600_bib35","first-page":"849","article-title":"On spectral clustering: Analysis and an algorithm","volume":"14","author":"Ng","year":"2001","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2022050612543777600_bib36","first-page":"178","article-title":"Stratified gesture recognition using the normalized longest common subsequence with rough sets","volume":"30","author":"Nyirarugira","year":"2015","journal-title":"Signal Processing: Image Communication"},{"key":"2022050612543777600_bib37","author":"OpenStreetMap contributors","year":"2017"},{"issue":"6","key":"2022050612543777600_bib38","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.3390\/e15062218","article-title":"Vessel pattern knowledge discovery from ais data: A framework for anomaly detection and route prediction","volume":"15","author":"Pallotta","year":"2013","journal-title":"Entropy"},{"key":"2022050612543777600_bib39","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.eswa.2016.09.015","article-title":"Mining regular behaviors based on multidimensional trajectories","volume":"66","author":"Pan","year":"2016","journal-title":"Expert Systems with Applications"},{"key":"2022050612543777600_bib40","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1049\/ic:20050084","article-title":"Toward event recognition using dynamic trajectory analysis and prediction","volume-title":"IEE International Symposium on Imaging for Crime Detection and Prevention (ICDP 2005)","author":"Piciarelli","year":"2005"},{"key":"2022050612543777600_bib41","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1016\/j.patrec.2006.02.004","article-title":"On-line trajectory clustering for anomalous events detection","volume":"27","author":"Piciarelli","year":"2006","journal-title":"Pattern Recognition Letters"},{"key":"2022050612543777600_bib42","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/ICME.2004.1394427","article-title":"Learning object trajectory patterns by spectral clustering","volume-title":"2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No. 04TH8763)","author":"Porikli","year":"2004"},{"key":"2022050612543777600_bib43","volume-title":"Trajectory distance metric using hidden Markov model based representation","author":"Porikli","year":"2004"},{"key":"2022050612543777600_bib44","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2004.335","article-title":"Event detection by eigenvector decomposition using object and frame features","volume-title":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","author":"Porikli","year":"2004"},{"key":"2022050612543777600_bib45","first-page":"1","article-title":"How good is my prediction? Finding a similarity measure for trajectory prediction evaluation [Review paper for trajectory distance\/similarity]","volume-title":"Proceedings of the IEEE Conference on Intelligent Transportation Systems, ITSC","author":"Quehl","year":"2018"},{"key":"2022050612543777600_bib46","first-page":"1","article-title":"Statistical analysis of motion patterns in ais data: Anomaly detection and motion prediction","volume-title":"2008 11th International Conference on Information Fusion","author":"Ristic","year":"2008"},{"issue":"6","key":"2022050612543777600_bib47","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1017\/S0373463313000519","article-title":"Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal","volume":"66","author":"Silveira","year":"2013","journal-title":"The Journal of Navigation"},{"key":"2022050612543777600_bib48","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.procs.2016.08.106","article-title":"Comparing and combining time series trajectories using dynamic time warping","volume":"96","author":"Vaughan","year":"2016","journal-title":"Procedia Computer Science"},{"key":"2022050612543777600_bib49","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1109\/ICDE.2002.994784","article-title":"Discovering similar multidimensional trajectories","volume-title":"Proceedings of the International Conference on Data Engineering","author":"Vlachos","year":"2002"},{"key":"2022050612543777600_bib50","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10462-016-9477-7","article-title":"A review of moving object trajectory clustering algorithms [Review paper for trajectory distance\/similarity]","volume":"47","author":"Yuan","year":"2017","journal-title":"Artificial Intelligence Review"},{"key":"2022050612543777600_bib51","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/ICPR.2006.392","article-title":"Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes","volume-title":"18th International Conference on Pattern Recognition (ICPR'06)","author":"Zhang","year":"2006"},{"key":"2022050612543777600_bib52","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1017\/S0373463316000850","article-title":"Maritime anomaly detection within coastal waters based on vessel trajectory clustering and na\u00efve Bayes classifier","volume":"70","author":"Zhen","year":"2017","journal-title":"Journal of Navigation"},{"key":"2022050612543777600_bib53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2743025","article-title":"Trajectory data mining: An overview","volume":"6","author":"Zheng","year":"2015","journal-title":"ACM Transactions on Intelligent Systems and Technology"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/9\/2\/731\/43589392\/qwac024.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/9\/2\/731\/43589392\/qwac024.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T04:21:16Z","timestamp":1700367676000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/9\/2\/731\/6568113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4,13]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwac024","relation":{},"ISSN":["2288-5048"],"issn-type":[{"value":"2288-5048","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,4]]},"published":{"date-parts":[[2022,4]]}}}