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 behavior (position, speed & 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 behavioral patterns that each vessel follows and successively use this as a means for predicting multiple vessel behavior 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 behavior 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

Vessel traffic management systems (VTMS) and vessel traffic monitoring information systems (VTMIS) have been available for a number of years now. These systems have significantly contributed to increasing the efficiency and safety of operations at sea. However, nowadays, risks at sea are once again on the rise, thus demanding an evolution in VTMS and VTMIS, such that they can support a human operator’s better understanding of the complex reality at sea and enhance his or her decision-making in light of danger. A critical requirement of such systems, is that they exhibit the ability to for-see unfolding cautious and potentially hazardous situations, so as to propose measures of danger avoidance. In this study, we employ machine learning, and specifically artificial neural networks, as a tool to add predictive capacity to VTMIS. The main objective of this study is to implement a publicly accessible, web-based system capable of real time learning and accurately predicting any vessels future behavior in low computational time. This work describes our approach, design choices, implementation and evaluation details, while we present a proof of concept prototype system. Our proposal can potentially be used as the predictive foundation for various intelligent systems, including vessel collision prevention, vessel route planning, operation efficiency estimation and even anomaly detection systems.