Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection

Kontopoulos, Ioannis; Varlamis, Iraklis; Tserpes, Konstantinos

doi:10.1007/978-3-030-38081-6_2

Cited by 11 publications

(13 citation statements)

References 12 publications

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“…For future work, the research could be evaluated by comparing it against the applications on marine transportation modeling. Direct comparison on clustering points in trajectories to define stages has been done in [5,53]. Indirect comparison on clustering trajectories has been done in [30,31].…”

Section: Enhanced Dbscan Algorithm Performance Evaluationmentioning

confidence: 99%

Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

2021

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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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Section: Enhanced Dbscan Algorithm Performance Evaluationmentioning

confidence: 99%

Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

2021

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“…Specifically, the findings of the proposed approach can be summarized in two major points: i) the resulting convex hulls manage to accurately represent the spatial boundaries of the vessel movement indicating that any future AIS position can be effectively predicted and placed in a certain geographic region ii) the convex hulls can effectively represent the behavior of the vessel movement in terms of speed and heading and detect possible anomalies and deviations. Although many early alerts and false alarms may arise, using the richer pattern information (direction and speed and position) as well as windowed averages of the latest vessel status it can be easier to distinguish true from false alarms (Kontopoulos et al 2019). It is among our future steps to further examine this problem.…”

Section: Conclusion and Next Stepsmentioning

confidence: 99%

“…In this direction maritime authorities can use this data for tracking vessels and analysing their behavior in order to early detect abnormalities, which may happen due to an engine failure, an accident in the sea, or on purpose. Building on the ideas of previous works in the field of anomalous vessel behaviour detection (Varlamis et al 2019, Kontopoulos et al 2019, this work focuses on the extraction of traffic patterns, which can be the basis for detecting such behaviours.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we extend Kontopoulos et al (2019) by employing concepts of the network abstraction model proposed in Varlamis et al (2019) and develop a methodology for processing multi-vessel AIS data more efficiently, using richer information on the network edges. The new network abstraction aims to extract multiple movement patterns for the same network edge, which correspond to a fine-grained clustering of the collected AIS data.…”

Section: Introductionmentioning

confidence: 99%

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A distributed framework for extracting maritime traffic patterns

Kontopoulos

Varlamis

Tserpes

2020

International Journal of Geographical Information Science

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All the modern surveillance systems take advantage of the Automatic Identification System (AIS), a compulsory tracking system for many types of vessels. Ships that carry AIS transponders on board transmit their position and status in order to alert nearby vessels and ground stations, but this information can well be used to identify events of interest and support decision making. The detection of anomalies (i.e. unexpected sailing behavior) in vessels' trajectories is such an event, which is of utmost importance. Approaches for detecting such anomalies vary from extracting normality models to searching for individual cases, such as AIS switch-off or collision avoidance maneuvers. The current research work follows the former method; it employs sparse historic AIS data and polynomial interpolation in order to extract shipping lanes. It modifies the DB-Scan clustering algorithm in order to achieve more coherent trajectory clusters, which are then composed to create the shipping lanes. The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers. The evaluation shows that the biggest part (i.e. more than 90%) of any future vessel trajectory falls within the extracted shipping lanes.

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“…Kontopoulos et al [9] proposed a clustering technique capable of efficiently modeling the behavior of vessels by using only free and openly transmitted Automatic Identification System (AIS) data. The use of AIS data allows the potential detection of illegal activities and provides real-time alerts to notify authorities of abnormal ship behavior.…”

Section: Status Detection In Transportationmentioning

confidence: 99%

Machine-Learning Approach to Analyze the Status of Forklift Vehicles with Irregular Movement in a Shipyard

Lee¹,

Lee²,

An³

et al. 2020

Preprint

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In large shipyards, the management of equipment, which are used for building a variety of ships, is critical. Because orders vary year to year, shipyard managers are required to determine methods to make the most of their limited resources. A particular difficulty that arises because of the nature and size of shipyards is the management of moving vehicles. In recent years, shipbuilding companies have attempted to manage and track the locations and movements of vehicles using Global Positioning System (GPS) modules. However, because certain vehicles, such as forklifts, roam irregularly around a yard, identifying their working status without being onsite is difficult. Location information alone is not sufficient to determine whether a vehicle is working, moving, waiting, or resting. This study proposes an approach based on machine learning to identify the work status of each forklift. We use the DBSCAN and k-means algorithms to identify the area in which a particular forklift is operating and the type of work it is performing. We developed a business intelligence system to collect information from forklifts equipped with GPS and Internet of Things (IoT) devices. The system provides visual information on the status of individual forklifts and helps in the efficient management of their movements within large shipyards.

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Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection

Cited by 11 publications

References 12 publications

Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

A distributed framework for extracting maritime traffic patterns

Machine-Learning Approach to Analyze the Status of Forklift Vehicles with Irregular Movement in a Shipyard

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