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Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

dc.contributor.authorFilipiak, Dominik
dc.contributor.authorWęcel, Krzysztof
dc.contributor.authorStróżyna, Milena
dc.contributor.authorMichalak, Michał
dc.contributor.authorAbramowicz, Witold
dc.date.accessioned2020-10-26T11:11:50Z
dc.date.available2020-10-26T11:11:50Z
dc.date.issued2020
dc.description.abstractThe presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network.de
dc.identifier.doi10.1007/s12599-020-00661-0
dc.identifier.pissn1867-0202
dc.identifier.urihttp://dx.doi.org/10.1007/s12599-020-00661-0
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34399
dc.publisherSpringer
dc.relation.ispartofBusiness & Information Systems Engineering: Vol. 62, No. 5
dc.relation.ispartofseriesBusiness & Information Systems Engineering
dc.subjectAIS
dc.subjectArtificial intelligence
dc.subjectGenetic algorithm
dc.subjectGraph discovery
dc.subjectMaritime traffic graph
dc.subjectMaritime traffic network
dc.subjectRoute planning
dc.subjectVessel routing
dc.subjectWaypoint discovery
dc.titleExtracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithmde
dc.typeText/Journal Article
gi.citation.endPage450
gi.citation.startPage435

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