Vessel and Port Efficiency Metrics through Validated AIS data
This work addresses data quality issues in maritime logistics for vessel operators and ports, enabling better optimization and environmental impact reduction, but it appears incremental as it builds on existing AIS data analytics.
The authors tackled the problem of unreliable AIS navigational data by developing a machine learning method to detect and correct errors, and introduced a metric for measuring vessel and port efficiency in business and environmental terms, demonstrated through a tool called PARES.
Automatic Identification System (AIS) data represents a rich source of information about maritime traffic and offers a great potential for data analytics and predictive modeling solutions, which can help optimizing logistic chains and to reduce 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.