Abstract
The analysis of vessel behaviors and ship-to-ship interactions in port areas is addressed in this paper by means of the probabilistic tool of Dynamic Bayesian Networks (DBNs). The dimensional reduction of the state space is pursued with Topology Representing Networks (TRNs), yielding the partitioning of the port area in zones of different size and shape. In the training phase, the zone changes of interacting moving vessels trigger different events, the occurrence of which is stored in Event-based DBNs. The interactions are modeled as deviation from the common behavior prescribed by a single-ship normality model, in order to reduce the number of conditional probabilities to calculate and store in the DBNs. Inference on the networks is then carried on to analyze the behavior of various ships and vessels maneuvering in the harbor. The results of the algorithm are showed by using simulated data relative to a real port.
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Castaldo, F., Palmieri, F.A.N., Regazzoni, C. (2015). Application of Bayesian Techniques to Behavior Analysis in Maritime Environments. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_17
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DOI: https://doi.org/10.1007/978-3-319-18164-6_17
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