Deep Learning & Graph Clustering for Maritime Logistics: Predicting Destination and Expected Time of Arrival for Vessels Across Europe

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2020-08-18
Department
Major/Subject
Data Science
Mcode
SCI3095
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
57+3
Series
Abstract
In recent years, the need for improving operational processes internationally has drastically increased in the maritime logistics field. The lack of streamlined systems that provide reliable information about real-time maritime traffic for the main agents across countries, such as ports operators and ships authorities, has prompted several research questions. In this work, we propose Deep learning and Machine Learning based methods for (i) clustering ports across Europe using their maritime traffic connectivity, (ii) predicting the next destination of vessels, and (iii) forecasting their expected voyage duration. Several experiments based on public AIS data are developed to analyse and verify these methods, and the results of these experiments indicate that the proposed models achieve the state-of-the-art predictive performance considering the wide geographical scope of the problem across all over Europe. Furthermore, a big advantage of the proposed methods respect to other solutions is that the input data configuration and the intrinsic nature of the models enable the users to predict the aforementioned targets about the next destination of vessels right after they arrive at any European port, instead of waiting for the information given by the first submitted AIS messages once their corresponding next voyage has started. When deployed into production, the resulting system will help maritime industry agents to enhance their real-time situational awareness and operational planning.
Description
Supervisor
Solin, Arno
Thesis advisor
Poikonen, Jussi
Keywords
maritime logistics, AI, deep learning, graph clustering
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