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A data-driven methodology for maritime Patterns of Life discovery

A data-driven methodology for maritime Patterns of Life discovery

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A data-driven methodology suitable for discovering maritime traffic patterns (maritime analytics) and revealing “roads of sea” is discussed. The proposed solution exploits the MapReduce paradigm to perform parallel distributed processing of large vessel tracking datasets, collected through the automatic identification system (AIS), and analyzes vessels' navigational patterns in a computationally efficient and accurate way. Unsupervised machine learning algorithms are employed to distinguish the spatiotemporal characteristics of vessel routes and distill global maritime “Patterns of Life.” A “Pattern of Life” emerges through an aggregated analysis of spatiotemporal directed port-to-port connections bearing thus the burden of post-processing analysis. Within this context, Patterns of Life are perceived as an enabler for numerous maritime applications. This work presents an overview of the ROute exTrActor (ROTA) approach, while focus is given toward the technical design aspects and challenges of this scheme. The merits of this work can be helpful to a wide spectrum of maritime services such as understanding and predicting vessels' activities, evaluating shipping's impact on the environment, detecting dangerous situations and providing risk reports, assessing the vessel's navigation performance and performing voyage optimization.

Chapter Contents:

  • 8.1 Introduction
  • 8.2 Related work
  • 8.3 Approach
  • 8.4 Applications
  • 8.4.1 Vessel's navigation performance and voyage optimization
  • 8.4.2 Shipping environmental impact
  • 8.4.3 Anomaly detection
  • 8.4.4 Autonomous ships
  • 8.4.5 Vessel activities identification
  • 8.5 Conclusions
  • References

Inspec keywords: unsupervised learning; marine navigation; data analysis; traffic engineering computing; parallel processing

Other keywords: data-driven methodology; voyage optimization; automatic identification system; roads of sea; spatiotemporal characteristics; vessel navigational patterns; ROute exTrActor; unsupervised machine learning algorithms; navigation performance; parallel distributed processing; post-processing analysis; maritime Patterns of Life discovery; MapReduce; large vessel tracking datasets; spatiotemporal directed port-to-port connections; aggregated analysis; maritime analytics

Subjects: Marine system control; Traffic engineering computing; Data handling techniques; Parallel software

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