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Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

Author

Listed:
  • Dominik Filipiak

    (Poznań University of Economics and Business)

  • Krzysztof Węcel

    (Poznań University of Economics and Business)

  • Milena Stróżyna

    (Poznań University of Economics and Business)

  • Michał Michalak
  • Witold Abramowicz

    (Poznań University of Economics and Business)

Abstract

The 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.

Suggested Citation

  • Dominik Filipiak & Krzysztof Węcel & Milena Stróżyna & Michał Michalak & Witold Abramowicz, 2020. "Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(5), pages 435-450, October.
    • Handle: RePEc:spr:binfse:v:62:y:2020:i:5:d:10.1007_s12599-020-00661-0
    DOI: 10.1007/s12599-020-00661-0
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    Cited by:

    1. Andreas Komninos & Charalampos Kostopoulos & John Garofalakis, 2022. "Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies," Information Technology & Tourism, Springer, vol. 24(2), pages 265-298, June.
    2. Mazurek, J. & Lu, L. & Krata, P. & Montewka, J. & Krata, H. & Kujala, P., 2022. "An updated method identifying collision-prone locations for ships. A case study for oil tankers navigating in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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