Conference paper Open Access
Tomaž Martinčič; Dejan Štepec; Joao Pita Costa; Kristijan Čagran; Athanasios Chaldeakis
Automatic Identification System (AIS) data represents a rich source of information about maritime traffic and offers a great potential for data analytics and predictive modelling solutions, which can help optimizing logistic chains and reducing environmental impacts. In this work, we address the main limitations of the validity of AIS navigational data fields, by proposing a machine learning-based data-driven methodology to detect and (to the possible extent) also correct erroneous data. Additionally, we propose a metric that can be used by vessel operators and ports to express numerically their business and environmental efficiency through time and spatial dimensions, enabled with the obtained validated AIS data. We also demonstrate Port Area Vessel Movements (PARES) tool, which demonstrates the proposed solutions.