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Detecting Outliers on Training Dataset for Better Quality of Estimation on Vessel Traces

Recently, data analysis methods have emerged to improve the safety of the maritime transport industry. The focus of these analyses frequently include estimation of arrival ports and arrival times. This paper introduces an estimation method for arrival ports and arrival times from ship trajectory data based on actual maritime data provided by Marin and the BigDataOcean Project of EU2020, and proposes a new outlier detection method from training data set, to enhance the quality of the estimation method. We use clustering techniques in existing studies to detect trajectory outliers. Our study focuses on selecting representative trajectories to detect parked vessels, and how to manipulate trajectory outlier values. To demonstrate the practicality of the proposed method, we apply the algorithm to the model of the existing study, which shows better quality of estimation on the destinations and the arrival time of the ships.