Vessel and Port Efficiency Metrics through Validated AIS data

TL;DR

This paper presents a machine learning approach to validate AIS maritime data and introduces a new metric for assessing vessel and port efficiency, supported by a practical tool for implementation.

Contribution

It introduces a data-driven methodology for correcting AIS data errors and proposes a novel efficiency metric for vessels and ports using validated data.

Findings

Effective error detection and correction in AIS data.

A new quantitative metric for vessel and port efficiency.

Demonstration of the PARES tool for practical application.

Abstract

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.