The growing availability of data coming from ship
reporting systems, such as Automatic Identification System (AIS), is originating
an unprecedented set of opportunities to enforce maritime
surveillance, ensure the security of the traffic at sea, and manage
maritime operations.
Following this fact, automatic estimation of vessel
times of arrival in port areas is an important matter. Adequate estimation leads to improve monitoring activities and eventually enhance the
port operations efficiency and safety. Although vessel ETA is available in the voyage-related part
of AIS messages, it is often unreliable because
it is manually input.
We propose cutting edge machine learning algorithms for estimation of vessel times of arrival based on AIS data.
Our framework presents novel methodologies for estimating the
vessel times of arrival in port areas by exploiting historical ship
reporting systems data based on a
data-driven path-finding algorithm, depending on a set of parameters
conveniently optimised for the area under investigation.
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