Vessel and Port Efficiency Metrics through Validated AIS data

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 mai...

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Bibliographic Details
Published inGlobal Oceans 2020: Singapore – U.S. Gulf Coast pp. 1 - 6
Main Authors Martincic, Tomaz, Stepec, Dejan, Costa, Joao Pita, Cagran, Kristijan, Chaldeakis, Athanasios
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.10.2020
Subjects
AIS
Artificial intelligence
covid-19
Data analysis
Data models
data validation
environment
Logistics
Measurement
port metrics
Predictive models
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Summary: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.
DOI:10.1109/IEEECONF38699.2020.9389112