K-means clustering for SAT-AIS data analysis

Mieczyńska, Marta; Czarnowski, Ireneusz

doi:10.1007/s13437-021-00241-3

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…With the advent of the Internet age, massive data is pouring in, and all kinds of information, such as orders, processing, transportation, etc. [10], must be processed accurately and effectively to organize the division of labor and form a whole integration. In addition, information infrastructure, as a hardware facility, is directly related to the ability to collect and process various data.…”

Section: Informationmentioning

confidence: 99%

Research on Location of Logistics Distribution Center Based on K-Means Clustering Algorithm

Wang

Chen

Zhang

2022

Security and Communication Networks

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The basic task of the logistics distribution center is to achieve the storage and distribution of materials, and to plan, implement, and manage the effective flow of materials from the supplying place to the consumption place. Scientific location of logistics distribution center can effectively reduce logistics cost, improve the speed of circulation, increase the profits of enterprises, and enhance the core competitiveness of enterprises. Combining the advantages of K-means clustering algorithm, this paper applies it to the location problem of logistics distribution center and proposes a logistics distribution center location method combining K-means clustering theory and D-S reasoning, which provides a better solution for the location problem of logistics distribution center. Through case analysis, K-means clustering algorithm can obtain reasonable location of logistics distribution center, which can be applied to the location of multilevel logistics distribution network, and has certain practical application value.

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Section: Informationmentioning

confidence: 99%

Research on Location of Logistics Distribution Center Based on K-Means Clustering Algorithm

Wang

Chen

Zhang

2022

Security and Communication Networks

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“…Thus, several studies were conducted to investigate research questions in the field of maritime traffic using historical AIS data. Examples of these studies include the detection of abnormal ship behavior [48], prediction of vessel trajectory [32,47], data analysis, such as outlier detection [5] and collision risk analysis [29], and the application of machine learning algorithms [46] to improve the quality of AIS data and enhance the performance of handling maritime processes.…”

Section: Related Workmentioning

confidence: 99%

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Gouareb

Can²,

Ferdowsi

et al. 2022

Lecture Notes in Computer Science

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As the world's population continues to expand, maritime transport is critical to ensure economic growth. To improve security and safety of maritime transportation, the Automatic Identification System AQ1 (AIS) collects real-time data about vessels and their positions. While a large portion of the AIS data is provided via an automatic tracking system, some key fields, such as destination and draught, are entered manually by the ship navigator and are thus prone to errors. To support decision making in maritime industries, in this paper we propose a datadriven vessel destination prediction algorithm based on heterogeneous graph and machine learning models. We design the task as a multi-class classification problem, where the destination port is the category to be predicted given the vessel and origin information. Then, we use a link prediction model in a weighted heterogeneous graph to predict the vessel destination. Experimental comparison against baseline methods, such as logistic regression and k-nearest neighbors, showed that our model provides a robust performance, outperforming the baseline algorithms by 9% and 33% in terms of accuracy and F1-score, respectively. Thus, heterogeneous graph models provide a powerful alternative to predict port destination, and could support enhancing AIS data quality and better decision making in maritime transportation industries.

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“…Zhang et al [36] proposed a ship route design based on AIS trajectory analysis. Mieczynska and Czarnowski [37] used clustering to improve AIS device efficiency, where they aimed to eliminate existing outliers resulting from AIS packet collision. Park and Jeong [38] estimated collision risk via distance-based parameters, such as distance at closest point approach (DCPA) and time to closest point approach (TCPA).…”

Section: Problem Statementmentioning

confidence: 99%

Risky Maritime Encounter Patterns via Clustering

Oruc

Altan

2023

JMSE

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The volume of maritime traffic is increasing with the growing global trade demand. The effect of volume growth is especially observed in narrow and congested waterways as an increase in the ship-ship encounters, which can have severe consequences such as collision. This study aims to analyze and validate the patterns of risky encounters and provide a framework for the visualization of model variables to explore patterns. Ship–ship interaction database is developed from the AIS messages, and interactions are analyzed via unsupervised learning algorithms to determine risky encounters using ship domain violation. K-means clustering-based novel methodology is developed to explore patterns among encounters. The methodology is applied to a long-term dataset from the Strait of Istanbul. Findings of the study support that ship length and ship speed can be used as indicators to understand the patterns in risky encounters. Furthermore, results show that site-specific risk thresholds for ship–ship encounters can be determined with additional expert judgment. The mid-clusters indicate that the ship domain violation is a grey zone, which should be treated carefully rather than a bold line. The developed approach can be integrated to narrow and congested waterways as an additional safety measure for maritime authorities to use as a decision support tool.

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K-means clustering for SAT-AIS data analysis

Cited by 10 publications

References 9 publications

Research on Location of Logistics Distribution Center Based on K-Means Clustering Algorithm

Research on Location of Logistics Distribution Center Based on K-Means Clustering Algorithm

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Risky Maritime Encounter Patterns via Clustering

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