Journal's Facebook pageMaritime Trajectory Mining: An Automatic Zones of Interests Discovery and Annotation Framework
- Aix-Marseille University, CNRS, LIS
Marseille, France
firstname.lastname@univ-amu.fr
Abstract
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.
Key words
Maritime Trajectory Mining, Zone Of Interest, Stops Extraction, Classification, Contextual Approach, OSM
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS240301063G
Publication information
Volume 21, Issue 4 (September 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
Ghannou, O., Thuillier, E., Boucelma, O.: Maritime Trajectory Mining: An Automatic Zones of Interests Discovery and Annotation Framework. Computer Science and Information Systems, Vol. 21, No. 4, 1963–1978. (2024), https://doi.org/10.2298/CSIS240301063G
