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Understanding evolution of maritime networks from automatic identification system data

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Abstract

Recent studies on maritime traffic model the interplay between vessels and ports as a graph, which is often built using automatic identification system (AIS) data. However, only a few works explicitly study the evolution of such graphs and, when they do, generally consider coarse-grained time intervals. Our goal is to fill this gap by providing a conceptual framework for the fine-grained systematic study of maritime graphs evolution. To this end, this paper presents the month-by-month analysis of world-wide graphs built using a 3-years AIS dataset. The analysis focuses on the evolution of several topological graph features, as well as their stationarity and statistical correlation. Results have revealed some interesting seasonal and trending patterns that can provide insights in the world-wide maritime context and be used as building blocks toward the prediction of graphs topology.

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Notes

  1. https://www.exactearth.com/products/exactais

  2. https://help.marinetraffic.com/

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Acknowledgment

The authors acknowledge the support of the H2020 EU Project MASTER (Multiple ASpects TrajEctoRy management and analysis) funded under the Marie Skłodowska-Curie grant agreement No 777695.

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Authors and Affiliations

  1. Institute of Information Science and Technologies, National Research Council (CNR), Pisa, Italy

    Emanuele Carlini & Vinicius Monteiro de Lira

  2. Department of Computer Science, Memorial University of Newfoundland, St. John’s, Canada

    Amilcar Soares

  3. Institute for Big Data Analytics, Dalhousie University, Halifax, Canada

    Mohammad Etemad, Bruno Brandoli & Stan Matwin

Authors
  1. Emanuele Carlini
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  2. Vinicius Monteiro de Lira
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  3. Amilcar Soares
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  4. Mohammad Etemad
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  5. Bruno Brandoli
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  6. Stan Matwin
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Correspondence to Emanuele Carlini.

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Carlini, E., de Lira, V., Soares, A. et al. Understanding evolution of maritime networks from automatic identification system data. Geoinformatica 26, 479–503 (2022). https://doi.org/10.1007/s10707-021-00451-0

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  • DOI: https://doi.org/10.1007/s10707-021-00451-0

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