A cloud based architecture capable of perceiving and predicting multiple vessel behaviour

• We train an Artificial Neural Network (ANN) to predict future vessel's behavior.
• We study if an ANN is capable of inferring the unique behaviour of a vessel.
• We design, train and implement a proof of concept ANN as a cloud based web app.
• The derived ANN has the ability to predict short and long term vessel behaviour.

Progressively huge amounts of data, tracking vessels during their voyages across the seas, are becoming available, mostly due to the automatic identification system (AIS) that vessels of specific categories are required to carry. These datasets provide detailed insights into the patterns vessels follow, while safely navigating across the globe, under various conditions. In this paper, we develop an Artificial Neural Network (ANN) capable of predicting a vessels future behaviour (position, speed and course), based on events that occur in a predictable pattern, across large map areas. The main concept of this study is to determine if an ANN is capable of inferring the unique behavioural patterns that each vessel follows and successively use this as a means for predicting multiple vessel behaviour into a future point in time. We design, train and implement a proof of concept ANN, as a cloud based web application, with the ability of overlaying predicted short and long term vessel behaviour on an interactive map. Our proposed approach could potentially assist in busy port scheduling, vessel route planning, anomaly detection and increasing overall Maritime Domain Awareness.

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