The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS

Abstract

To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand.

Recent Advances in Underwater Signal Processing

“…In [2], researchers address the storage, management, analysis, and mining of ship target data. It designs and develops the overall structure and functional modules of the Ship Trajectory Data Management and Analysis System (STDMAS), proposing a ship identification method based on motion characteristics.…”

Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime Systems

“…Increasing Vessel Traffic: The continuous growth of global trade has led to an increase in vessel traffic, resulting in congestion, especially in crucial maritime zones such as the Strait of Malacca, the English Channel, and near major port areas [21]. Managing this rising traffic effectively to avoid collisions, groundings, and delays is a critical challenge.…”

Identification of Spoofing Ships from Automatic Identification System Data via Trajectory Segmentation and Isolation Forest

“…When a ship is monitored by AIS and radar before and after MMSI spoofing, both monitoring processes continue for a period of time, meaning the MMSI spoofing algorithm demonstrates a good performance [25]. Iphar et al propose a rule-based method for data integrity assessment, with rules built from the system technical specifications and by domain experts, formalized by a logic-based framework, resulting in the triggering of situation-specific alerts [26][27][28]. Jeong et al provided an automatic shipping route construction method using functional data analysis (FDA), and the proposed approach includes two steps: outlier detection and shipping route construction [29].…”