DBSCAN algorithm for AIS data reconstruction

“…If a point is so distant from other points from a given dataset that the clustering algorithm puts it into a standalone, 1-element cluster, it should also be considered an anomaly. The performance of using DBSCAN algorithm in this stage, as well as hyperparameter tuning, is presented in [Mieczyńska and Czarnowski 2021].…”

Section: Proposed Approach For Damaged Ais Data Reconstructionmentioning

confidence: 99%

Reconstructing Damaged Data in AIS and Other Telecommunications Systems: A Survey

Szarmach,

Czarnowski

2023

SJGMU

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AIS (Automatic Identification System) is a telecommunication system created to enable ships to transmit information regarding their trajectories (such as their position, speed, course, etc.) to other ships and shore stations. With the use of AIS, collisions between ships can be avoided. Unfortunately, AIS suffers from some technical issues that lead to part of the transmitted data being damaged (incorrect or missing). This paper contains a review of machine learning based methods of reconstructing this damaged AIS data as well as examples of inspiration from other telecommunication systems for dealing with this kind of a problem. Finally, after analysing frameworks available in the relevant literature, a novel algorithm for AIS data reconstruction is briefly presented.

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“…In addition, a model for analyzing ship route patterns and predicting dangerous situations using DBSCAN was introduced [22], and the ship trajectory anomaly detection models such as unexpected stop, deviation from the prescribed route, or inconsistent speed, were proposed [23]. The DBSCAN algorithm was also used in another case as a method to reconstruct the damaged or missing AIS coordinate data [24]. Recently, to predict the spread of COVID-19, the AIS data and DBSCAN algorithm were used, and they helped predict infectious diseases in ports and neighboring countries [25].…”

Section: Data Clusteringmentioning

confidence: 99%

Development of Priority Index for Intelligent Vessel Traffic Monitoring System in Vessel Traffic Service Areas

Lee

Kim

2022

Applied Sciences

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Recognizing dangerous situations in advance and determining priority is essential in vessel traffic surveillance. The traffic management priority is determined by the vessel traffic service operator (VTSO) employing the closest point of approach (CPA) and the time to CPA (TCPA) of the targets considering their current navigational data. Various environmental conditions influence CPA and TCPA, which affects the importance of surveillance. This study aims to support vessel traffic prioritization in the navigation surveillance of VTSO from the observer side. The vessel tracks were clustered based on density, and a priority index of the vessel surveillance was developed in the VTS area by reflecting regional navigation characteristics. Density-based spatial clustering of applications with noise (DBSCAN) was used for data clustering to classify the surveillance area. A fuzzy membership function was constructed based on the CPA and TCPA belonging to each cluster, and a dataset for determining priorities was constructed, yielding 17 clusters, fuzzy rules, and tables, with the priority index extracted for all vessel pairs to visualize the priority. The results indicated prior recognition of all dangerous situations. The proposed method facilitates vessel surveillance priority determination in high-density areas and predicts the risk in advance, thereby contributing to traffic management.

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DBSCAN algorithm for AIS data reconstruction

Cited by 13 publications

References 11 publications

A close contact identification algorithm using kernel density estimation for the ship passenger health

A close contact identification algorithm using kernel density estimation for the ship passenger health

Reconstructing Damaged Data in AIS and Other Telecommunications Systems: A Survey

Development of Priority Index for Intelligent Vessel Traffic Monitoring System in Vessel Traffic Service Areas

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