Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

Filipiak, Dominik; Węcel, Krzysztof; Stróżyna, Milena; Michalak, Michał; Abramowicz, Witold

doi:10.1007/s12599-020-00661-0

Cited by 46 publications

(16 citation statements)

References 43 publications

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“…To determine more realistic representations of viable sea routing graphs, researchers have very recently turned to the availability of Automatic Identification System data, which contains continuous information on vessel position and type (Filipiak et al. 2020 ; Sheng and Yin 2018 ). This data allow the construction of navigable graphs, which closely match actual practice as demonstrated by real vessels.…”

Section: Related Workmentioning

confidence: 99%

Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies

Komninos

Kostopoulos²,

Garofalakis

2022

Inf Technol Tourism

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Sailing holiday activities represent a significant portion of the Blue Economy growth in Europe and across the world. Due to the global financial crisis, yacht ownership has declined, but demand for such holiday products remained steady, therefore shifting the yachters profile towards younger and less experienced consumers who prefer to charter boats, rather than own one. Boat chartering offers more flexibility to explore different regions from year to year, but this means that significantly more time must be spent planning the route, since local experience is absent. The tourists’ experience during the initial contemplation and planning phase, taking place weeks or months before an actual trip, and where a broad range of route options needs to be explored, could thus significantly benefit from support given by automated IT tools. Current literature demonstrates a complete lack of research in the development of itinerary recommendation systems in the context of sailing holidays. In this paper, we describe a methodology for the automatic generation of route recommendations, based on the semantic modelling of spatial data, and the determination of realistic sea route options, based on vessel density maps produced from raw AIS data. We demonstrate the implementation and results from this methodology using one of the most popular sailing regions of Greece, namely the Ionian Sea, as a case study.

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Section: Related Workmentioning

confidence: 99%

Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies

Komninos

Kostopoulos²,

Garofalakis

2022

Inf Technol Tourism

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“…Empirical results indicate that the compression efficiency may be higher than with the default parametrization (Fikioris et al, 2020). Filipiak et al described how to derive sea routes from AIS data using a parallel genetic algorithm coupled with KD-B trees (Filipiak et al, 2020). Based on historical AIS trajectories, Han and Yang derived the main channel of a particular sea area, constructed a topological channel network by preserving the channel's geometric characteristics, and found the route of the ships through a grid-channel network .…”

Section: Ship Routing and Trajectory Predictionmentioning

confidence: 99%

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Aylak¹

2022

European Journal of Science and Technology

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This study aims to identify current approaches in the usage of Artificial Intelligence (AI) methods for solving shipping problems. Recent advances in AI are being examined, and the way it is adapted to maritime logistics is reviewed. In this study, 66 papers dealing with AI in the maritime industry are reviewed bibliometrically. Research data were primarily sourced from databases of IEEE Xplore, Web of Science, ScienceDirect (Elsevier), Sciences Citation Index, Google Scholar, Springer, and journals. Selected papers are categorized and classified, and the outcomes of some noteworthy publications are discussed in detail. A comprehensive assessment is also presented, which highlights research gaps and forecasts future research orientations. Two possible areas in the maritime industry are proposed for further research using AI capabilities. Predictive analysis is the first domain, followed by energy efficiency optimization. In addition, Machine Learning (ML) and Operations Research (OR) have fostered a growing interest in automating the learning of heuristics to solve optimization problems to avoid the need for expensive and inefficient human labour to create highly specialized heuristics. Future research can take advantage of these new ML approaches to address Maritime Logistics problems utilizing the ever-increasing amount of data available. Future research on maritime logistics can also develop learning models based on the identified gaps.

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“…Many studies employ a port‐to‐port network representation [27], which includes origins, destinations, and direct paths between origins and destinations [12, 13]. While a port‐to‐port network is useful in supporting high‐level large‐scale maritime planning practices, a fine level network representation including waypoints of routes is desirable to enable berth‐to‐berth voyage planning, traffic monitoring in coastal and open waters, and regional transportation management [10, 11, 28]. Besides, many studies with a few exceptions (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Sequence‐based centrality measures in maritime transportation networks

Li¹,

Wang

Zhang

2020

IET intell. transp. syst

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Performing centrality analysis on nodes from transportation networks are critical to identify important hubs, understand travel decisions, and assess system performances. Current centrality measures are based on topological characteristics of nodes and edges. When applying those measures to large-scale transportation networks, two problems remain unsolved. First, measures are computed based on simplified travel paths, which only include origins and destinations. Due to the lack of information about waypoints of routes, such network representation may not preserve fine level information about waypoints, routes, and traffic flow patterns, resulting in an inaccurate view of centrality. Second, most centrality measures are global measures that rank all nodes in a network, thus failing to detect nodes of regional importance. Therefore, this paper describes an approach that leverages the concept of sequences to identify key waypoints from frequent travel paths and detect community structures of transportation networks. This approach extends two complementary centrality measures to define the role of nodes within communities. The approach has been tested using tracking data of ships in a regional maritime transportation network. Compared to traditional measurement approaches, the proposed approach can construct compact communities, discover prominent waypoints, and add new insight with local centrality measure.

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Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

Cited by 46 publications

References 43 publications

Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies

Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Sequence‐based centrality measures in maritime transportation networks

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