Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events

Vouros, George A.; Vlachou, Akrivi; Santipantakis, Georgios M.; Doulkeridis, Christos; Pelekis, Nikos; Georgiou, Harris V.; Theodoridis, Yannis; Patroumpas, Kostas; Alevizos, Elias; Artikis, Alexander; Fuchs, Georg; Mock, Michael; Andrienko, Gennady; Claramunt, Christophe; Ray, Cyril; Camossi, Elena; Jousselme, Anne-Laure

doi:10.1007/978-3-319-90053-7_13

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Such consumer modules include analytics components, such as trajectory predictors and complex event recognition engines, and we refer to [48] for a detailed overview of the overall architecture of datAcron. The primary data in the derived stream is surveillance data of moving objects.…”

Section: Problem Settingmentioning

confidence: 99%

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

Santipantakis

Glenis

Patroumpas

et al. 2020

Future Generation Computer Systems

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An ever-increasing number of applications in critical domains, such as maritime and aviation, generate, collect, manage and process spatio-temporal data related to the mobility of entities. This wealth of data can be exploited for various purposes, towards improving the safety of operations, reducing economical costs, and increasing dependability: The major issue to achieve these objectives is increasing predictability of moving objects' trajectories and events. To achieve this purpose in a data-driven way we need to exploit in integrated manners data from a variety of disparate and heterogeneous data sources, both streaming and archival, regarding -among other -surveillance, weather, and contextual data.Motivated by this fact, in this paper, we propose a framework for semantic integration of big mobility data with other data sources that are necessary to data analytics tasks, providing a unified representation of such data. Notable features of our framework include the real-time generation of data synopses of moving entities' trajectories, the efficient and flexible transformation of data from heterogeneous and big data sources in RDF, and the spatio-temporal link discovery between spatio-temporal entities in diverse data sources. The design and implementation of our framework uses big data technologies (Apache Flink and Kafka), and our experimental evaluation demonstrates the efficiency and scalability of the proposed framework using large, real-life datasets. The ever-increasing size of spatio-temporal data and the unprecedented rate of data generation from a wide variety of sources regarding the situation awareness and monitoring in critical domains raise the need for scalable, real-time management and analysis of mobility data. Several data analysis tasks rely on moving entities' trajectories, while trajectory detection and prediction are typically used to optimize everyday, real-life operations. However, using only the kinematic information provided by surveillance sources is far from sufficient, when at the same time a wealth of other sources, including for instance, weather and contextual 1 information is available too. Consequently, one of the major challenges is to enrich surveillance data, providing meaningful information about moving entities' trajectories, also annotating trajectories with related events, thereby creating enriched trajectories [1, 2]. Addressing this challenge calls for real-time processing and semantic integration of surveillance data with other, streaming and archival, data sources [3].Our work is motivated by the need to advance the management and integrated exploitation of voluminous and heterogeneous data-at-rest (archival data) and data-in-motion (streaming data) sources, so as to significantly promote safety and effectiveness of critical operations for large numbers of moving entities in large geographical areas. Challenges throughout the Big Data ecosystem, with special focus on surveillance systems, concern effective detection and 1 By contextual data, we refer to data other...

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Section: Problem Settingmentioning

confidence: 99%

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

Santipantakis

Glenis

Patroumpas

et al. 2020

Future Generation Computer Systems

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“…The idea is to generate recommended routes after summarizing and analyzing a large amount of ship navigation data. Some scholars process AIS data to recognize key turning regions and connect these turning regions via cluster similarity measuring to generate reasonable routes for different types of ships [25,26]. Others try to use artificial neural networks and machine learning algorithms to predict the fuel consumption of a ship under different sailing conditions to achieve the goal of determining the expected duration or saving energy [27].…”

Section: Introductionmentioning

confidence: 99%

A Ship Route Planning Method under the Sailing Time Constraint

Cui

Zhang

et al. 2023

JMSE

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This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions.

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“…However, the usage of S-AIS data has still been rarely considered in the research of arrival planning, delay detection and terminal organization. Nevertheless, ongoing projects address integration of services and optimized operations such as Sea Traffic management (STM) [17] and Big Data Analytics for Time Critical Mobility Forecasting (datAcron) [28].…”

Section: Introductionmentioning

confidence: 99%

Near Real-time S-AIS

Mrzljak

Žarković

Poljak³

2018

Pomorstvo (Online)

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The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.

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Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events

Cited by 7 publications

References 10 publications

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

A Ship Route Planning Method under the Sailing Time Constraint

Near Real-time S-AIS

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