Understanding evolution of maritime networks from automatic identification system data

Carlini E., De Lira V. M., Soares A., Etemad M., Brandoli B., Matwin S.

Graph Analysis  Bigdata  Trajectories 

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.

Source: Geoinformatica (Dordrecht) (2021). doi:10.1007/s10707-021-00451-0

Publisher: Kluwer Academic Publishers, Dordrecht , Paesi Bassi

Metrics