Abnormal Ship Behavior Detection after the Closure of AIS Based on Radar Data

In marine transportation, most ships are equipped with AIS devices. The AIS data sent by these devices can help maritime authorities to manage ships in relevant sea areas. However, AIS is a self-reporting system, when a ship is engaged in illegal activities, the AIS device may be turned off. Therefore, after the AIS is closed, if the ship's behavior during a certain period of time is different from the ship's behavior before the closure of AIS, the different behavior is likely to represent that the ship is conducting illegal activities. This behavior is considered abnormal and needs to be detected in time. Based on radar trajectory data, the detection of abnormal ship behavior is studied from two aspects: speed and direction. In order to improve the intelligent level of abnormal ship behavior detection, the abnormal speed behavior detection algorithm combined with rules and clustering (ASBD-RC) and the abnormal direction behavior detection algorithm combined with partition and the earth mover's distance (ADBD-PE) are proposed. The ASBD-RC algorithm can reduce the influence of noise and sea clutter on abnormal speed behavior detection. The ADBD-PE algorithm can effectively partition and identify trajectory segments with abnormal direction. In the experiment, based on the real and simulated radar trajectories, the abnormal behaviors of ships under different scenarios are generated. The experimental results show that in most scenarios, the ASBD-RC algorithm and the ADBD-PE algorithm can effectively detect abnormal ship behavior. And compared with other algorithms, the proposed two algorithms have better and more stable detection results.

A Visual Analysis Approach to Understand and Explore Quality Problems of AIS Data

Low quality automatic identification system (AIS) data often mislead analysts to a misunderstanding of ship behavior analysis and to making incorrect navigation risk assessments. It is therefore necessary to accurately understand and judge the quality problems in AIS data before a further analysis of ship behavior. Outliers were filtered in the existing methods of AIS quality analysis based only on mathematical models where AIS data related quality problems are not utilized and there is a lack of visual exploration. Thus, the human brain’s ability cannot be fully utilized to think visually and for reasoning. In this regard, a visual analytics (VA) approach called AIS Data Quality visualization (ADQvis) was designed and implemented here to support evaluations and explorations of AIS data quality. The system interface is overviewed and then the visualization model and corresponding human-computer interaction method are described in detail. Finally, case studies were carried out to demonstrate the effectiveness of our visual analytics approach for AIS quality problems.

Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.