Navigation Decision Support: Discover of Vessel Traffic Anomaly According to the Historic Marine Data

Authors

  • Andrius Daranda Vilnius University
  • Gintautas Dzemyda VU Institute of Data Science and Digital Technologies Vilnius University, Lithuania

Keywords:

marine anomaly detection, marine traffic, spatial data, DBSCAN, clustering, k-nearest neighbors, regression

Abstract

During the last years, marine traffic dramatically increases. Marine traffic safety highly depends on the mariner’s decisions and particular situations. The watch officer must continuously observe the marine traffic for anomalies because the anomaly detection is crucial to predict dangerous situations and to make a decision in time for safe marine navigation. In this paper, we present marine traffic anomaly detection by the combination of the DBSCAN clustering algorithm (Density- Based Spatial Clustering of Applications with Noise) with k-nearest neighbors analysis among the clusters and particular vessels. The clustering algorithm is applied to the historic marine traffic data — a set of vessel turn points. In our experiments, the total number of turn points was about 3 million, and about 160 megabytes of computer store was used. A formal numerical criterion to com-pare anomaly with normal traffic flow case has been proposed. It gives us a possibility to detect the vessels outside the typical traffic pattern. The proposed meth-od ensures the right decisions in different oceanic scale or hydro meteorology conditions in the detection of anomaly situation of the vessel.

References

Brusch, S.; Lehner, S.; Fritz, T.; Soccorsi, M.; Soloviev, A.; Van Schie, B. (2011). Ship surveillance with TerraSAR-X, IEEE Transactions on Geoscience and Remote Sensing, 49, 1092-1103, 2011. https://doi.org/10.1109/TGRS.2010.2071879

Celik, M.; Cebi, S. (2009). Analytical HFACS for investigating human errors in shipping accidents, Accident Analysis and Prevention, 41, 66-75, 2009. https://doi.org/10.1016/j.aap.2008.09.004

Chen, S.T.; Wall, A.; Davies, P.; Yang, Z.; Wang, J., Chou, Y.H. (2013). A Human and Organisational Factors (HOFs) analysis method for marine casualties using HFACS-Maritime Accidents (HFACS-MA), Safety Science, 60, 105-114 (2013). https://doi.org/10.1016/j.ssci.2013.06.009

Daranda, A.; Andziulis, J.S. (2015). Fake vessels identification in the AIS, In: Transport Means 2015 Proceedings, 248-252, 2015.

Eriksen, T.; Høye, G.; Narheim, B.; Meland, B.J. (2016). Maritime traffic monitoring using a space-based AIS receiver, In: International Astronautical Federation - 55th International Astronautical Congress, 5276-5289, 2004.

Ester, M.; Kriegel, H.-P.; Sander, J.; Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise, In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 226-231, 1996.

Fournier, M.; Casey Hilliard, R.; Rezaee, S.; Pelot, R. (2018). Past, present, and future of the satellite-based automatic identification system: areas of applications (2004-2016), WMU Journal of Maritime Affairs, 17, 311-345, 2018. https://doi.org/10.1007/s13437-018-0151-6

Fujii, Y.; Shiobara, R. (1971). The analysis of traffic accidents, Journal of Navigation, 24, 534-543, 1971. https://doi.org/10.1017/S0373463300022372

Gaugel, T.; Mittag, J.; Hartenstein, H. et al. (2019). In-depth analysis and evaluation of Selforganizing TDMA, In: 2013 IEEE Vehicular Networking Conference, VNC, Boston, 79-86, 2013. https://doi.org/10.1109/VNC.2013.6737593

Goerlandt, F.; Goite, H.; Valdez Banda, O.A.; Höglund, A.; Ahonen-Rainio, P.; Lensu, M. (2017). An analysis of wintertime navigational accidents in the Northern Baltic Sea, Safety Science, 92, 66-84, 2017. https://doi.org/10.1016/j.ssci.2016.09.011

Hodge, V.J.; Austin, J. (2018). A survey of outlier detection methodologies, Artificial Intelligence Review, 22, 85-126, 2004. https://doi.org/10.1023/B:AIRE.0000045502.10941.a9

International Telecommunication Union (ITU) (2014). Technical characteristics for an automatic identification system using time division multiple access in the VHF maritime mobile frequency band M Series Mobile, radiodetermination, amateur and related satellite, 2014.

Jin, M.; Shi, W.; Lin, K.C.; Li, K.X. (2019). Marine piracy prediction and prevention: Policy implications, Marine Policy, 108, 2019. https://doi.org/10.1016/j.marpol.2019.103528

Longépé, N.; Hajduch, G.; Ardianto, R. et al. (2018). Completing fishing monitoring with spaceborne Vessel Detection System (VDS) and Automatic Identification System (AIS) to assess illegal fishing in Indonesia, Marine Pollution Bulletin, 131, 33-39, 2018. https://doi.org/10.1016/j.marpolbul.2017.10.016

Mazaheri, A.; Montewka, J.; Kujala, P. (2013). Correlation between the ship grounding accident and the ship traffic - A case study based on the statistics of the Gulf of Finland, TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 7, 119-124, 2013. https://doi.org/10.12716/1001.07.01.16

Prabowo, A.R.; Bae, D.M. (2019). Environmental risk of maritime territory subjected to accidental phenomena: Correlation of oil spill and ship grounding in the Exxon Valdez's case, Results in Engineering, 4, 100035, 2019. https://doi.org/10.1016/j.rineng.2019.100035

Tsou, M.C. (2016). Online analysis process on Automatic Identification System data warehouse for application in vessel traffic service, Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment, 230, 199-215, 2016. https://doi.org/10.1177/1475090214541426

Venskus, J.; Treigys, P.; Bernatavicien˙e, J.; Tamulevicius, G.; Medvedev, V. (2019). Realtime maritime traffic anomaly detection based on sensors and history data embedding, Sensors (Switzerland), 19, 3782, 2019. https://doi.org/10.3390/s19173782

Wang, Y.; Han, L.; Liu, W.; Yang, S.; Gao, Y. (2019). Study on wavelet neural network based anomaly detection in ocean observing data series, Ocean Engineering, 186, 2019. https://doi.org/10.1016/j.oceaneng.2019.106129

[Online] IMO Resolution MSC 74 (69), Annex 3, Recommendation on Performance Standards for an Universal Shipborne Automatic Identification System (AIS), http://www.imo.org/en/KnowledgeCentre/IndexofIMOResolutions/Maritime-Safety- Committee-(MSC)/Documents/MSC.74(69).pdf, Accesed on 10 December 2019.

Published

2020-04-21

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.