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http://dx.doi.org/10.5394/KINPR.2018.42.4.277

Detection of Ship Movement Anomaly using AIS Data: A Study  

Oh, Jae-Yong (Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering)
Kim, Hye-Jin (Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering)
Park, Se-Kil (Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering)
Publication Information
Journal of Navigation and Port Research / v.42, no.4, 2018 , pp. 277-282 More about this Journal
Abstract
Recently, the Vessel Traffic Service (VTS) coverage has expanded to include coastal areas following the increased attention on vessel traffic safety. However, it has increased the workload on the VTS operators. In some cases, when the traffic volume increases sharply during the rush hour, the VTS operator may not be aware of the risks. Therefore, in this paper, we proposed a new method to recognize ship movement anomalies automatically to support the VTS operator's decision-making. The proposed method generated traffic pattern model without any category information using the unsupervised learning algorithm.. The anomaly score can be calculated by classification and comparison of the trained model. Finally, we reviewed the experimental results using a ship-handling simulator and the actual trajectory data to verify the feasibility of the proposed method.
Keywords
Machine Learning; AIS; Maritime Traffic Analysis; Ship Movement Anomaly; Vessel Traffic Service;
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