Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.

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Abstract. Identification of vessel motion pattern from large amount of maritime data can help to high level contextual information and improve the effectiveness of surveillance technologies. Vessel routes belonged to certain motion pattern can provide useful information on daily patterns and transit duration. Therefore an approach to identify motion pattern is presented. In paper, the distance similarity matrix of the trajectory dataset was constructed by using the measurement method in trajectory with one-way distance. The regular motion patterns of vessels were extracted from the trajectories spatial distribution learnt by the spectral clustering algorithm. Finally motion patterns of vessel traveling in Qiongzhou strait was extracted using the proposed method. The results showed that the method has high precision on clustering the vessel trajectories and is applicable to identify movement patterns of vessels in maritime areas such as coastal ports, narrow waterway and traffic complex area.

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