Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring

Arguedas, Virginia Fernández; Pallotta, Giuliana; Vespe, Michele

doi:10.1109/tits.2017.2699635

Cited by 110 publications

(41 citation statements)

References 11 publications

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“…The annual amount of cargo is about 1 billion tonnes, and the ferries carry approximately 100 million passengers. The main research applications have been general spatiotemporal characterization and statistical analyses of the traffic systems in some sea areas [19] and the quantification of ship encounters and associated collision risks (e.g., [20]).…”

Section: Introductionmentioning

confidence: 99%

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Similä

Lensu

2018

Remote Sensing

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The automatic identification system (AIS) was developed to support the safety of marine traffic. In ice-covered seas, the ship speeds extracted from AIS data vary with ice conditions that are simultaneously reflected by features in synthetic aperture radar (SAR) images. In this study, the speed variation was related to the SAR features and the results were applied to generate a chart of expected speeds from the SAR image. The study was done in the Gulf of Bothnia in March 2013 for ships with ice class IA Super that are able to navigate without icebreaker assistance. The speeds were normalized to dimensionless units ranging from 0 to 10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap (from hours to two days), we calculated a set of local statistical SAR features over several scales. Random forest tree regression was used to estimate the speed. The accuracy was quantified by mean squared error and by the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. The error varied from 0.4 to 2.7 units 2 for daily routes. Sixty-five percent of the estimates deviated by less than one speed unit and 82% by less than 1.5 speed units from the AIS speeds. The estimated daily mean speeds were close to the observations. The largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when the ice chart thickness was included as a predictor.

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Section: Introductionmentioning

confidence: 99%

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Similä

Lensu

2018

Remote Sensing

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“…Despite having been specified in the late part of the twentieth century, it has only gained popularity over the last decade due to the use of satellite-based receivers which provide global coverage, improved response times and more reliability [37]. Space-based AIS receptions open the possibility of unmanned transoceanic journeys, convenient for the transport of hazardous materials, which consequently enables the elongation of the duration of non-time-critical journeys, optimizes the fuel consumption or even allows the direct use of electrical or solar power [38], [39]. Additionally, these satellites serve as supplementary data sources for vessels and coastal authorities in busy port areas where conventional AIS receivers may not be able to cope with the large volume of ocean traffic [40].…”

Section: Aeronautical and Maritime Tracking And Communicationmentioning

confidence: 99%

Satellite Communications in the New Space Era: A Survey and Future Challenges

Kodheli

Lagunas

Maturo

et al. 2021

IEEE Commun. Surv. Tutorials

679

428

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Satellite communications (SatComs) have recently entered a period of renewed interest motivated by technological advances and nurtured through private investment and ventures. The present survey aims at capturing the state of the art in SatComs, while highlighting the most promising open research topics. Firstly, the main innovation drivers are motivated, such as new constellation types, on-board processing capabilities, nonterrestrial networks and space-based data collection/processing. Secondly, the most promising applications are described i.e. 5G integration, space communications, Earth observation, aeronautical and maritime tracking and communication. Subsequently, an in-depth literature review is provided across five axes: i) system aspects, ii) air interface, iii) medium access, iv) networking, v) testbeds & prototyping. Finally, a number of future challenges and the respective open research topics are described.

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“…Many works the recent years try to build maritime traffic network representations from historical AIS data [5,6]. Arguedas et al [5] propose a two-layer network: (i) an external layer that uses way-points as nodes/vertices and routes as edges/lines and (ii) an internal layer that consists of nodes or breakpoints that represent vessels' changes in behavior and edges or tracklets that represent vessel trajectories. The former layer is a traffic network abstraction, while the latter is a network that provides information about each vessel layer individually.…”

Section: Related Workmentioning

confidence: 99%

“…It is characteristic that the use of the 454 complete port-to-port routes in the small area of the Baltic sea resulted in an internal layer with 2, 095 tracklets. Our proposed model is similar to that of the external layer of [5] but provides a much richer internal layer, that maintains statistical information extracted from the trajectories of the sailing vessels. The resulting model significantly reduces the total amount of data contributed originally by the vessels, without loosing its descriptive power.…”

Section: Related Workmentioning

confidence: 99%

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

Kontopoulos

Varlamis

Tserpes

2020

Lecture Notes in Computer Science

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The compulsory use of Automatic Identification System (AIS) for many vessel types, which has been enforced by naval regulations, has opened new opportunities for maritime surveillance. AIS transponders are rich sources of information that everyone can collect using an RF receiver and provide real-time information about vessels' position. Properly taking advantage of AIS data, can uncover potential illegal behavior, offer real-time alerts and notify the authorities for any kind of anomalous vessel behavior. In this article, we extend an existing network abstraction of maritime traffic, that is based on nodes (called way-points) that correspond to naval areas of long stays or major turns for vessels (e.g. ports, capes, offshore platforms etc.) and edges (called traversals) that correspond to the routes followed by vessels between two consecutive way-points. The current work, focuses on the connections of this network abstraction and enriches them with semantic information about the different ways that vessels employ when traversing an edge. For achieving this, it proposes an alternative of the popular density based clustering algorithm DB-Scan, which modifies the proximity parameter (i.e. epsilon) of the algorithm. The proposed alternative employs in tandem the difference in (i) speed, (ii) course and (iii) position for defining the distance between two consecutive vessel positions (two consecutive AIS signals received from the same vessel). The results show that this combination performs significantly better than using only the spatial distance and, more importantly, results in clusters that have very interesting properties. The enriched network model can be processed and further examined with data mining techniques, even in an unsupervised manner, in order to identify anomalies in vessels' trajectories. Experimental results on a real dataset show the network's potential for detecting trajectory outliers and uncovering deviations on a vessel's route.

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Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring

Cited by 110 publications

References 11 publications

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Satellite Communications in the New Space Era: A Survey and Future Challenges

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

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