An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm

Hyeong-Tak Lee; Jeong-Seok Lee; Hyun Yang; Ik-Soon Cho

doi:10.3390/app11020799

An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm

Applied Sciences ◽

10.3390/app11020799 ◽

2021 ◽

Vol 11 (2) ◽

pp. 799

Author(s):

Hyeong-Tak Lee ◽

Jeong-Seok Lee ◽

Hyun Yang ◽

Ik-Soon Cho

Keyword(s):

Human Error ◽

Significant Contribution ◽

Pattern Analysis ◽

Spatial Clustering ◽

Data Driven ◽

Automatic Identification ◽

Maritime Industry ◽

Clustering Method ◽

Dbscan Algorithm ◽

Data Driven Approach

As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems’ data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port.

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Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060351 ◽

2020 ◽

Vol 9 (6) ◽

pp. 351 ◽

Cited By ~ 2

Author(s):

Zhihuan Wang ◽

Mengyuan Yao ◽

Chenguang Meng ◽

Christophe Claramunt

Keyword(s):

Spatial Clustering ◽

Infection Risk ◽

Data Driven ◽

Automatic Identification ◽

Quick Response ◽

Support Mechanism ◽

Risk Levels ◽

Data Driven Approach ◽

Spatio Temporal ◽

And Control

Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.

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A hybrid spatial data clustering method for site selection: The data driven approach of GIS mining

Expert Systems with Applications ◽

10.1016/j.eswa.2008.02.056 ◽

2009 ◽

Vol 36 (2) ◽

pp. 3923-3936 ◽

Cited By ~ 13

Author(s):

Bo Fan

Keyword(s):

Spatial Data ◽

Site Selection ◽

Data Clustering ◽

Data Driven ◽

Clustering Method ◽

Data Driven Approach

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Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Journal of Marine Science and Engineering ◽

10.3390/jmse9060566 ◽

2021 ◽

Vol 9 (6) ◽

pp. 566

Author(s):

Lianhui Wang ◽

Pengfei Chen ◽

Linying Chen ◽

Junmin Mou

Keyword(s):

Traffic Management ◽

Performance Metrics ◽

Spatial Clustering ◽

Maritime Transportation ◽

Automatic Identification ◽

Identification System ◽

Clustering Methods ◽

Trajectory Clustering ◽

Clustering Method ◽

Shape Characteristics

The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

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Dynamic Data Driven Approach for Modeling Human Error

Procedia Computer Science ◽

10.1016/j.procs.2015.05.298 ◽

2015 ◽

Vol 51 ◽

pp. 1643-1654 ◽

Cited By ~ 7

Author(s):

Wan-Lin Hu ◽

Janette J. Meyer ◽

Zhaosen Wang ◽

Tahira Reid ◽

Douglas E. Adams ◽

...

Keyword(s):

Human Error ◽

Data Driven ◽

Dynamic Data ◽

Data Driven Approach

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Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

Sustainability ◽

10.3390/su13158162 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8162

Author(s):

Xuyang Han ◽

Costas Armenakis ◽

Mojgan Jadidi

Keyword(s):

Situational Awareness ◽

Spatial Clustering ◽

Route Planning ◽

Maritime Transportation ◽

Automatic Identification ◽

Identification System ◽

Marine Transportation ◽

Dbscan Clustering ◽

Clustering Model ◽

Data Driven Approach

Today, maritime transportation represents a substantial portion of international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this paper, we present an enhanced density-based spatial clustering of applications with noise (DBSCAN) method to model vessel behaviours based on trajectory point data. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis distance metric, which considers the correlation between the points representing vessel locations. This research proposes applying the clustering method to historical Automatic Identification System (AIS) data using an algorithm to generate a clustering model of the vessels’ trajectories and a model for detecting vessel trajectory anomalies, such as unexpected stops, deviations from regulated routes, or inconsistent speed. Further, an automatic and data-driven approach is proposed to select the initial parameters for the enhanced DBSCAN approach. Results are presented from two case studies using an openly available Gulf of Mexico AIS dataset as well as a Saint Lawrence Seaway and Great Lakes AIS licensed dataset acquired from ORBCOMM (a maritime AIS data provider). These research findings demonstrate the applicability and scalability of the proposed method for modeling more water regions, contributing to situational awareness, vessel collision prevention, safe navigation, route planning, and detection of vessel behaviour anomalies for auto-vessel development towards the sustainability of marine transportation.

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DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b4-2020-455-2020 ◽

2020 ◽

Vol XLIII-B4-2020 ◽

pp. 455-461

Author(s):

X. Han ◽

C. Armenakis ◽

M. Jadidi

Keyword(s):

Human Error ◽

Spatial Clustering ◽

Autonomous Systems ◽

Computational Cost ◽

Route Planning ◽

Maritime Transportation ◽

Automatic Identification ◽

Identification System ◽

Trajectory Clustering ◽

Clustering Model

Abstract. Today maritime transportation represents 90% of international trade volume and there are more than 50,000 vessels sailing the ocean every day. Therefore, reducing maritime transportation security risks by systematically modelling and surveillance should be of high priority in the maritime domain. By statistics, majority of maritime accidents are caused by human error due to fatigue or misjudgment. Auto-vessels equipped with autonomous and semi-autonomous systems can reduce the reliance on human’s intervention, thus make maritime navigation safer. This paper presents a clustering method for route planning and trajectory anomalies detection, which are the essential part of auto-vessel system design and development. In this paper, we present the development of an enhanced density-based spatial clustering (DBSCAN) method that can be applied on historical or real-time Automatic Identification System (AIS) data, so that vessel routes can be modelled, and the trajectories’ anomalies can be detected. The proposed methodology is based on developing an optimized trajectory clustering approach in two stages. Firstly, to increase the attribute dimension of the vessel’s positioning data, therefore other characteristics such as velocity and direction are considered in the clustering process along with geospatial information. Secondly, the DBSCAN clustering model has been enhanced by introducing the Mahalanobis Distance metric considering the correlations of the position cluster points aiming to make the identification process more accurate as well as reducing the computational cost.

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