Computer Science and Information Systems 2024 Volume 21, Issue 4, Pages: 1963-1978
https://doi.org/10.2298/CSIS240301063G
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Maritime trajectory mining: An automatic zones of interests discovery and annotation framework⋆

Ghannou Omar (Aix-Marseille University, CNRS, LIS, Marseille, France)
Thuillier Etienne (Aix-Marseille University, CNRS, LIS, Marseille, France)
Boucelma Omar (Aix-Marseille University, CNRS, LIS, Marseille, France)

As global traffic continues to grow, the identification of areas of particular significance, known as Zones of Interest (ZOI), becomes crucial for optimizing transportation systems and analyzing mobility patterns. In the maritime domain, effective ZOIs discovery is essential for enhancing route planning, improving safety measures, and managing resources efficiently. Within the context of trajectory mining, these ZOIs provide valuable insights into movement behaviors and operational efficiencies. In this paper, we present a framework for discovering and annotating ZOIs within maritime trajectories. The proposed approach involves processing raw positional data to initially identify candidate ZOIs, which are subsequently refined using contextual information. By leveraging real georeferenced vessels trajectories, collected from thousands of commercial ships, this framework proposes a structure of elements that will be implemented as part of the TNTM French project. While this research contributes to maritime field by providing a method for ZOIs discovery and annotation, it can be generalized to various application domains that may leverage of mobility data analytics.

Keywords: Maritime Trajectory Mining, Zone Of Interest, ZOI, Area Of Interest, AOI, Stops Extraction, Classification, Contextual Approach, OpenStreetMap, OSM, VGI

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