Unexpected trajectory detection based on the geometrical features of AIS-generated ship tracks
Wijaya, W.M.; Nakamura, Y. (2024). Unexpected trajectory detection based on the geometrical features of AIS-generated ship tracks. International Journal of Advanced Computer Science and Applications 15(7): 1442-1450. https://dx.doi.org/10.14569/ijacsa.2024.01507140
In: International Journal of Advanced Computer Science and Applications. The Science and Information: New York, NY. ISSN 2158-107X; e-ISSN 2156-5570, more
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Automatic identification system; vessel trajectory classification; unexpected behavior detection; data-driven decision support |
| Authors | | Top |
- Wijaya, W.M.
- Nakamura, Y.
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| Abstract |
Due to the efficiency and reliability of delivering goods by ships, maritime transport has been the backbone of global trade. In normal circumstances, a ship’s voyage is expected to assure the safety of life at sea, efficient and safe navigation, and protection of the maritime environment. However, ships may demonstrate unexpected behavior due to certain situations, such as machinery malfunction, unexpected bad weather, and other emergencies, as well as involvement in illicit activities. These situations pose threats to the safety and security of maritime transport. The expansion of the threats makes manual surveillance inefficient, which involves extensive labor and is prone to oversight. Thus, automated surveillance systems are required. This paper proposes a method to detect the unexpected behavior of ships based on the Automatic Identification System (AIS) data. The method exploits the geometrical features of AIS-generated trajectories to identify unexpected trajectory, which could be a deviation from the common routes, loitering, or both deviating and loitering. It introduces novel formulas for calculating trajectory redundancy and curvature features. The DBSCAN clustering is applied based on the features to classify trajectories as expected or unexpected. Unlike existing methods, the proposed technique does not require trajectory-to-image conversion or training of labeled datasets. The technique was tested on real-world AIS data from the South China Sea, Western Indonesia, Singapore, and Malaysian waters between July 2021 and February 2022. The experimental results demonstrate the method’s feasibility in detecting deviating and loitering behaviors. Evaluation on a labeled dataset shows superior performance compared to existing loitering detection methods across multiple metrics, with 99% accuracy and 100% precision in identifying loitering trajectories. The proposed method aims to provide maritime authorities and fleet owners with an efficient tool for monitoring ship behaviors in real time regarding safety, security, and economic concerns. |
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