{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:30:39Z","timestamp":1772253039380,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T00:00:00Z","timestamp":1567209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel\u2019s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.<\/jats:p>","DOI":"10.3390\/s19173782","type":"journal-article","created":{"date-parts":[[2019,9,2]],"date-time":"2019-09-02T03:16:12Z","timestamp":1567394172000},"page":"3782","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding"],"prefix":"10.3390","volume":"19","author":[{"given":"Julius","family":"Venskus","sequence":"first","affiliation":[{"name":"Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08412 Vilnius, Lithuania"}]},{"given":"Povilas","family":"Treigys","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08412 Vilnius, Lithuania"}]},{"given":"Jolita","family":"Bernatavi\u010dien\u0117","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08412 Vilnius, Lithuania"}]},{"given":"Gintautas","family":"Tamulevi\u010dius","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08412 Vilnius, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5813-8545","authenticated-orcid":false,"given":"Viktor","family":"Medvedev","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08412 Vilnius, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,31]]},"reference":[{"key":"ref_1","unstructured":"The European Commission (2019, August 29). Maritime Transport Statistics-Short Sea Shipping of Goods. Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php\/Maritime_transport_statistics_-_short_sea_shipping_of_goods."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1038\/540027a","article-title":"Four routes to better maritime governance","volume":"540","author":"Wan","year":"2016","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7898","DOI":"10.1109\/ACCESS.2017.2698208","article-title":"Finding Abnormal Vessel Trajectories Using Feature Learning","volume":"5","author":"Fu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_4","unstructured":"Will, J., Peel, L., and Claxton, C. (2011, January 20). Fast maritime anomaly detection using kd-tree gaussian processes. Proceedings of the IMA Maths in Defence Conference, Swindon, UK."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lane, R.O., Nevell, D.A., Hayward, S.D., and Beaney, T.W. (2010, January 26\u201329). Maritime anomaly detection and threat assessment. Proceedings of the FUSION 2010: 13th International Conference on Information Fusion, Edinburgh, UK.","DOI":"10.1109\/ICIF.2010.5711998"},{"key":"ref_6","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":"Shu","year":"2017","journal-title":"J. Navig."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1109\/TAES.2013.130377","article-title":"Detecting Anomalies from a Multitarget Tracking Output","volume":"50","author":"Ristic","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhu, F. (2011, January 8). Mining ship spatial trajectory patterns from AIS database for maritime surveillance. Proceedings of the 2nd IEEE International Conference on Emergency Management and Management Sciences, Beijing, China.","DOI":"10.1109\/ICEMMS.2011.6015796"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Deng, F., Guo, S., Deng, Y., Chu, H., Zhu, Q., and Sun, F. (2014, January 28\u201329). Vessel track information mining using AIS data. Proceedings of the International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), Beijing, China.","DOI":"10.1109\/MFI.2014.6997641"},{"key":"ref_10","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":"ref_11","doi-asserted-by":"crossref","unstructured":"Arguedas, V.F., Mazzarella, F., and Vespe, M. (2015, January 18\u201321). Spatio-temporal data mining for maritime situational awareness. Proceedings of the OCEANS 2015-Genova, Genoa, Italy.","DOI":"10.1109\/OCEANS-Genova.2015.7271544"},{"key":"ref_12","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":"J. Navig."},{"key":"ref_13","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":"Knowl. Inf. Syst."},{"key":"ref_14","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":"J. Navig."},{"key":"ref_15","first-page":"1","article-title":"Maritime anomaly detection: A review","volume":"2018","author":"Riveiro","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1017\/S0373463317000546","article-title":"Research on Ship Classification Based on Trajectory Features","volume":"71","author":"Sheng","year":"2018","journal-title":"J. Navig."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.engappai.2017.06.005","article-title":"Context-based behaviour modelling and classification of marine vessels in an abalone poaching situation","volume":"64","author":"Dabrowski","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13426","DOI":"10.1016\/j.eswa.2012.05.060","article-title":"Machine learning for vessel trajectories using compression, alignments and domain knowledge","volume":"39","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_19","unstructured":"Soleimani, B.H., Souza, E.N.D., Hilliard, C., and Matwin, S. (2015, January 6\u20139). Anomaly detection in maritime data based on geometrical analysis of trajectories. Proceedings of the 18th International Conference on Information Fusion (Fusion), Washington, DC, USA."},{"key":"ref_20","unstructured":"Radon, A.N., Wang, K., Gl\u00e4sser, U., Wehn, H., and Westwell-Roper, A. (November, January 29). Contextual verification for false alarm reduction in maritime anomaly detection. Proceedings of the IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s10472-013-9381-7","article-title":"Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories","volume":"74","author":"Laxhammar","year":"2015","journal-title":"Ann. Math. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gammerman, A., Vovk, V., and Papadopoulos, H. (2015). Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. Statistical Learning and Data Sciences, Springer International Publishing.","DOI":"10.1007\/978-3-319-17091-6"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cazzanti, L., and Pallotta, G. (2015, January 18\u201321). Mining maritime vessel traffic: Promises, challenges, techniques. Proceedings of the OCEANS 2015-Genova, Genoa, Italy.","DOI":"10.1109\/OCEANS-Genova.2015.7271555"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0925-2312(98)00030-7","article-title":"The self-organizing map","volume":"21","author":"Kohonen","year":"1998","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.engappai.2006.09.005","article-title":"A hierarchical SOM-based intrusion detection system","volume":"20","author":"Kayacik","year":"2007","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115","DOI":"10.15388\/Informatica.2011.317","article-title":"Quality of quantization and visualization of vectors obtained by neural gas and self-organizing map","volume":"22","author":"Kurasova","year":"2011","journal-title":"Informatica"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dzemyda, G., Kurasova, O., and \u017dilinskas, J. (2012). Multidimensional Data Visualization: Methods and Applications, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-0236-8"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.simpat.2017.03.001","article-title":"A new web-based solution for modelling data mining processes","volume":"76","author":"Medvedev","year":"2017","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"359","DOI":"10.15388\/Informatica.2017.133","article-title":"Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic","volume":"28","author":"Venskus","year":"2017","journal-title":"Informatica"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.engappai.2018.07.005","article-title":"Application of new training methods for neural model reference control","volume":"74","author":"Jafari","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1109\/TVLSI.2017.2717950","article-title":"Accelerating recurrent neural networks: A memory-efficient approach","volume":"25","author":"Wang","year":"2017","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1016\/j.fusengdes.2011.01.069","article-title":"Disruption prediction with adaptive neural networks for ASDEX Upgrade","volume":"86","author":"Cannas","year":"2011","journal-title":"Fusion Eng. Des."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1109\/TIA.2017.2661250","article-title":"Deep learning based approach for bearing fault diagnosis","volume":"53","author":"He","year":"2017","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8","DOI":"10.15837\/ijccc.2015.1.1310","article-title":"Method for visual detection of similarities in medical streaming data","volume":"10","author":"Bernataviciene","year":"2015","journal-title":"Int. J. Comput. Commun. Control."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3782\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:15:37Z","timestamp":1760188537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3782"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,31]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["s19173782"],"URL":"https:\/\/doi.org\/10.3390\/s19173782","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1038\/s41598-025-34353-0","asserted-by":"object"}]},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,31]]}}}