Hierarchical maritime traffic network generation from large AIS datasets: application to the West Mediterranean Sea case

Abstract

Abstract We present a hierarchical scalable methodology to analyze the maritime traffic of year 2022 associated with cargo, tanker, and passenger vessels traveling within the West Mediterranean Sea. The methodology is based on data-mining and machine learning techniques to process large Automatic Identification System (AIS) datasets. We present both a high-level representation of network traffic for the global area and low-level networks for local areas. The intermediate steps of the methodology generate relevant results about critical aspects of maritime traffic. We explore a large test matrix to assess the impact of tuning parameters upon the results and to propose practical guidelines for the methodology application in congested areas. The described approach highlights the traffic features of each ship category at different levels of detail and may support maritime traffic planning and management activity.