Pasquale Coscia - Unsupervised Maritime Traffic Graph Learning with Mean-Reverting Stochastic

Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation
Pasquale Coscia, Paolo Braca, Leonardo M. Millefiori, Francesco A. N. Palmieri and Peter K. Willett
21st International Conference on Information Fusion (FUSION 2018)

Abstract

Inspired by the fair regularity of the motion of ships, we present a method to derive a representation of the commercial maritime traffic in the form of a graph, whose nodes represent way-point areas, or regions of likely direction changes, and whose edges represent navigational legs with constant cruise velocity. The proposed method is based on the representation of a ship’s velocity with an Ornstein-Uhlenbeck process and on the detection of changes of its long-run mean to identify navigational way-points. In order to assess the graph representativeness of the traffic, two performance metrics are introduced, leading to distinct graph construction criteria. Finally, the proposed method is validated against real-world Automatic Identification System data collected in a large area.

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Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph RepresentationPasquale Coscia, Paolo Braca, Leonardo M. Millefiori, Francesco A. N. Palmieri and Peter K. Willett21st International Conference on Information Fusion (FUSION 2018)

Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation
Pasquale Coscia, Paolo Braca, Leonardo M. Millefiori, Francesco A. N. Palmieri and Peter K. Willett
21st International Conference on Information Fusion (FUSION 2018)