The paper proposes a probabilistic framework for assessing the risk of ships based on a
hybrid approach and multiple data sources. A Bayes-based network learning approach uses …

A data mining approach is presented for probabilistic characterization of maritime traffic and
anomaly detection. The approach automatically groups historical traffic data provided by the …

This paper proposes a novel maritime traffic prediction method based on ship motion pattern
extraction, considering ship destination prediction and ship trajectory prediction within a …

This paper proposes a fuzzy logic-based intelligent decision-making approach for
navigation strategy selection in the inland traffic separation scheme. The dynamic …

The operational modes of ships reflect the status of ship engines and thus greatly impact the
rate of emissions. To capture the characteristics of ship emissions, this study proposes an …

Abstract Maritime Spatial Planning (MSP) has become of key interest in many countries
around the world in the last decade, leading to an increased need to inform the historical …

The situational awareness in intersection waterways should be improved given the
numerous accidents occurring in these areas. We propose a deep learning model for …

This paper proposes a framework to assess the influence of Offshore Wind Farms (OWFs) on
maritime traffic flow based on raw Automatic Identification System (AIS) data collected before …

This paper aims to investigate the U-turn made by ships far away from their destination
docks (called off-destination-dock U-turns) in the narrow waterway using automatic …

With the recent increase in the use of sea transportation, the importance of maritime
surveillance for detecting unusual vessel behavior related to several illegal activities has …