{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T10:16:00Z","timestamp":1778667360428,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFC2801002"],"award-info":[{"award-number":["2021YFC2801002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Illegal transshipment of maritime ships is usually closely related to illegal activities such as smuggling, human trafficking, piracy plunder, and illegal fishing. Intelligent identification of illegal transshipment has become an important technical means to ensure the safety of maritime transport. However, due to different geographical environments, legal policies and regulatory requirements in each sea area, there are differences in the movement characteristics and geographical distribution of illegal transshipment behavior in different time and space. Moreover, in areas with dense traffic flow, normal navigation behavior can easily be identified as illegal transshipment, resulting in a high rate of misidentification. This paper proposes a hybrid rule-based and data-driven approach to solve the problem of missing identification in fixed threshold methods and introduces a traffic density feature to reduce the misidentification rate in dense traffic areas. The method is both interpretable and adaptable through unsupervised clustering to get suitable threshold distribution combination for regulatory sea areas. The evaluation results in two different sea areas show that the proposed method is applicable. Compared with other widely used identification methods, this method identifies more illegal transshipment events, which are highly suspicious, and gives warning much earlier. The proposed method can even filter out misidentification events from compared methods\u2019 results, which account for more than half of the total number.<\/jats:p>","DOI":"10.3390\/s22249581","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T05:50:52Z","timestamp":1670392252000},"page":"9581","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Hybrid Rule-Based and Data-Driven Approach to Illegal Transshipment Identification with Interpretable Behavior Features"],"prefix":"10.3390","volume":"22","author":[{"given":"Lei","family":"Deng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"},{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchen","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limin","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8311-9004","authenticated-orcid":false,"given":"Wen","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Transportation Safety and Emergency Informatics, China Transport Telecommunications and Information Center, Beijing 100011, China"},{"name":"China Transport Informatics National Engineering Laboratory Co., Ltd., Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8995-9389","authenticated-orcid":false,"given":"Yu","family":"Zang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Transportation Safety and Emergency Informatics, China Transport Telecommunications and Information Center, Beijing 100011, China"},{"name":"China Transport Informatics National Engineering Laboratory Co., Ltd., Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Canton, H. (2021). United Nations Conference on Trade and Development\u2014UNCTAD. The Europa Directory of International Organizations 2021, Routledge. Available online: https:\/\/unctad.org\/webflyer\/review-maritime-transport-2021.","DOI":"10.4324\/9781003179900-26"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1080\/10246029.2020.1728352","article-title":"Transnational organised crime at sea as a threat to the sustainable development goals: Taking direction from piracy and counter-piracy in the Western Indian Ocean","volume":"28","author":"Bruwer","year":"2019","journal-title":"Afr. Secur. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3389\/fmars.2018.00240","article-title":"Identifying global patterns of transshipment behavior","volume":"5","author":"Miller","year":"2018","journal-title":"Front. Mar. Sci."},{"key":"ref_4","unstructured":"FAO (2016). The state of world fisheries and aquaculture 2016. Contributing to Food Security and Nutrition for All, Food and Agriculture Organization of the United Nations. Available online: https:\/\/www.fao.org\/publications."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1163\/15718080120493236","article-title":"The international plan of action on illegal unreported and unregulated fishing: The legal context of a non-legally binding instrument","volume":"16","author":"Edeson","year":"2001","journal-title":"Int. J. Mar. Coast. Law"},{"key":"ref_6","unstructured":"McDowell, R., Mason, M., and Mendoza, M. (2022, November 06). AP investigation: Slaves May Have Caught the Fish You Bought, Yahoo News, Available online: https:\/\/www.ap.org\/explore\/seafood-from-slaves\/ap-investigation-slaves-may-have-caught-the-fish-you-bought.html."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.marpol.2017.04.004","article-title":"Potential ecological and social benefits of a moratorium on transshipment on the high seas","volume":"81","author":"Christopher","year":"2017","journal-title":"Mar. Policy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1016\/j.marpol.2010.03.002","article-title":"Failing the high seas: A global evaluation of regional fisheries management organizations","volume":"34","author":"Sarika","year":"2010","journal-title":"Mar. Policy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1017\/S0373463317000066","article-title":"Study of automatic anomalous behaviour detection techniques for maritime vessels","volume":"70","author":"Abdoulaye","year":"2017","journal-title":"J. Navig."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.marpol.2018.10.027","article-title":"Techno-optimism and ocean governance: New trends in maritime monitoring","volume":"99","author":"Nyman","year":"2019","journal-title":"Mar. Policy"},{"key":"ref_11","first-page":"1972","article-title":"A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis","volume":"17","author":"Huanhuan","year":"2017","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1080\/01441647.2019.1649315","article-title":"How big data enriches maritime research\u2014A critical review of Automatic Identification System(AIS) data applications","volume":"39","author":"Dong","year":"2019","journal-title":"Transp. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.marpol.2018.06.010","article-title":"Characterizing transhipment at-sea activities by longline and purse seine fisheries in response to recent policy changes in Indonesia","volume":"95","author":"Satria","year":"2018","journal-title":"Mar. Policy"},{"key":"ref_14","unstructured":"Western Commission, C.P.F. (2009). Conservation and Management Measure 2009-06: Regulation of Transhipment. Wcpfc Sixth Regul. Sess., 29, 1\u20138."},{"key":"ref_15","unstructured":"CCAMLR (2016). Conservation Measure 10-05 (2016): Catch Documentation Scheme for Dissostichus spp.. CCAMLR, 5, 3\u20138."},{"key":"ref_16","unstructured":"SEAFO (2019). SEAFO System of Observation, Inspection, Compliance and Enforcement, South East Atlantic Fisheries Organization. Available online: http:\/\/www.seafo.org\/."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pitsikalis, M., Artikis, A., Dreo, R., Ray, C., Camossi, E., and Jousselme, A.L. (2019, January 24\u201328). Composite event recognition for maritime monitoring. Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems, Darmstadt, Germany.","DOI":"10.1145\/3328905.3329762"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Riveiro, M., Falkman, G., Ziemke, T., and Warston, H. (2009, January 13). VISAD: An interactive and visual analytical tool for the detection of behavioral anomalies in maritime traffic data. Proceedings of the Visual Analytics for Homeland Defense and Security, Orlando, FL, USA.","DOI":"10.1117\/12.817819"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.inffus.2013.03.004","article-title":"Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks","volume":"21","author":"Snidaro","year":"2015","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nguyen, V., and Mellor, L. (2020, January 6\u20139). Fuzzy MLNs and QSTAGs for activity recognition and modelling with rush. Proceedings of the 2020 IEEE 23rd International Conference on Information Fusion (FUSION), Rustenburg, South Africa.","DOI":"10.23919\/FUSION45008.2020.9190523"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Masroeri, A., Aisjah, A.S., Agam, V., Pradenta, M., and Samudya, M.A. (2021, January 27). Analysis of fuzzy logic systems types 1 and 2 in identifying of IUU fishing and transshipment: A case study in Indonesia\u2019s vulnerable waters. Proceedings of the 6th International Conference on Marine Technology (SENTA 2021), Surabaya, Indonesia.","DOI":"10.1088\/1755-1315\/972\/1\/012060"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Troupiotis-Kapeliaris, A., Chatzikokolakis, K., Zissis, D., and Alevizos, E. (July, January 30). Experimental Comparison of Complex Event Processing Systems in the Maritime Domain. Proceedings of the 2020 21st IEEE International Conference on Mobile Data Management (MDM), Versailles, France.","DOI":"10.1109\/MDM48529.2020.00066"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1287\/inte.2019.0997","article-title":"MPA-IBM project SAFER: Sense-making analytics for maritime event recognition","volume":"49","author":"Yeo","year":"2019","journal-title":"Informs J. Appl. Anal."},{"key":"ref_24","unstructured":"Petry, L.M., Soares, A., Bogorny, V., and Matwin, S. (2019). Unsupervised behavior change detection in multidimensional data streams for maritime traffic monitoring. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1109\/TKDE.2014.2356476","article-title":"An event calculus for event recognition","volume":"27","author":"Artikis","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bock, H.H. (2007). Clustering methods: A history of K-means algorithms. Selected Contributions in Data Analysis and Classification, Institute of Statistics, RWTH Aachen University.","DOI":"10.1007\/978-3-540-73560-1_15"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ray, C., Dr\u00e9o, R., Camossi, E., and Jousselme, A.L. (2018). Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance. Data Brief.","DOI":"10.1016\/j.dib.2019.104141"},{"key":"ref_28","unstructured":"Pitsikalis, M., and Artikis, A. (2022, November 06). Composite Maritime Events. Available online: https:\/\/www.zenodo.org\/record\/2557290#.Y4tk77ZByt8."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104141","DOI":"10.1016\/j.dib.2019.104141","article-title":"Heterogeneous integrated dataset for maritime intelligence, surveillance, and reconnaissance","volume":"25","author":"Ray","year":"2019","journal-title":"Data Brief"},{"key":"ref_30","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9581\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:35:37Z","timestamp":1760146537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,7]]},"references-count":30,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249581"],"URL":"https:\/\/doi.org\/10.3390\/s22249581","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,7]]}}}