Vessel and Port Efficiency Metrics through Validated AIS data
| Autor: | Athanasios Chaldeakis, Tomaz Martincic, Dejan Stepec, Joao Pita Costa, Kristijan Cagran |
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| Jazyk: | angličtina |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 010504 meteorology & atmospheric sciences Automatic Identification System Computer science Computer Science - Artificial Intelligence Data field 010501 environmental sciences computer.software_genre Environmental efficiency 01 natural sciences Port (computer networking) law.invention Machine Learning (cs.LG) Artificial Intelligence (cs.AI) law Metric (mathematics) Data analysis 14. Life underwater Data mining computer 0105 earth and related environmental sciences |
| Zdroj: | Global Oceans 2020: Singapore – U.S. Gulf Coast |
| DOI: | 10.1109/ieeeconf38699.2020.9389112 |
| Popis: | 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. Comment: OCEANS 2020 |
| Databáze: | OpenAIRE |
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