System Engineering and Productivity

System Engineering and Productivity

Anomaly Detection in Maritime Traffic Based on Spatial-temporal Data from Automatic Identification System (AIS)

Document Type : Research Paper

Authors
Mojtaba Goudarzi 1
Mahdi Shabani 2
1 Corresponding author: M.Sc., Faculty of Electrical, Computer and Mechanical Engineering, University
2 Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
10.22034/sep.2022.243396
Abstract
Monitoring maritime traffic is an important aspect of safety and security, especially in busy sea lanes and near-dock traffic. In this study, a two-part anomaly detection model for maritime traffic is proposed. In the first part, the typical pattern of ship trajectories and routes, which are collected and made publicly available, is identified using clustering algorithms and IMO density-based rules in data mining. The most important advantage of density-based clustering at this stage is the identification of ship movement patterns according to the rules and conditions in the maritime area. In the second part, the proposed model, based on the pattern identified in the first part and using a point-based approach, identifies abnormal ship movements at sea. For clustering and identification of anomalous behavior, longitude and latitude, speed and direction at each point of the route traveled by the ships are used. The point-based anomaly detection approach is also applicable to real-time systems. In this research, the monitoring detection (AIS) stage of the automatic anomaly detection system can be customized by the monitoring decision makers, which results in the accuracy of anomaly detection being adjustable.
Keywords
Anomaly detection
Density-based clustering
Trajectory data mining
Marine surveillance

Copyright ©, Mojtaba Goudarzi, Mahdi Shabani

 

License

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System Engineering and Productivity
Volume 1, Issue 1 - Serial Number 1
Serial No. 1, Winter Quarterly
Winter 2022
Pages 73-97