Next Article in Journal
FPGA Implementation of ECT Digital System for Imaging Conductive Materials
Next Article in Special Issue
An INS-UWB Based Collision Avoidance System for AGV
Previous Article in Journal
Ensemble and Deep Learning for Language-Independent Automatic Selection of Parallel Data
Previous Article in Special Issue
A Forecast Model of the Number of Containers for Containership Voyage
Open AccessArticle

Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data

Department of Information and Communication Systems Engineering, University of the Aegean, GR-83200 Samos, Greece
Algorithms 2019, 12(1), 27;
Received: 31 October 2018 / Revised: 4 January 2019 / Accepted: 17 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue Modeling Computing and Data Handling for Marine Transportation)
PDF [5966 KB, uploaded 22 January 2019]


Marine transportation in Aegean Sea, a part of the Mediterranean Sea that serves as gateway between three continents has recently seen a significant increase. Despite the commercial benefits to the region, there are certain issues related to the preservation of the local ecosystem and safety. This danger is further deteriorated by the absence of regulations on allowed waterways. Marine accidents could cause a major ecological disaster in the area and pose big socio-economic impacts in Greece. Monitoring marine traffic data is of major importance and one of the primary goals of the current research. Real-time monitoring and alerting can be extremely useful to local authorities, companies, NGO’s and the public in general. Apart from real-time applications, the knowledge discovery from historical data is also significant. Towards this direction, a data analysis and simulation framework for maritime data has been designed and developed. The framework analyzes historical data about ships and area conditions, of varying time and space granularity, measures critical parameters that could influence the levels of hazard in certain regions and clusters such data according to their similarity. Upon this unsupervised step, the degree of hazard is estimated and along with other important parameters is fed into a special type of Bayesian network, in order to infer on future situations, thus, simulating future data based on past conditions. Another innovative aspect of this work is the modeling of shipping traffic as a social network, whose analysis could provide useful and informative visualizations. The use of such a system is particularly beneficial for multiple stakeholders, such as the port authorities, the ministry of Mercantile Marine, etc. mainly due to the fact that specific policy options can be evaluated and re-designed based on feedback from our framework. View Full-Text
Keywords: maritime transportation; data engineering; modeling and simulation; Bayesian networks; social network analysis maritime transportation; data engineering; modeling and simulation; Bayesian networks; social network analysis

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Maragoudakis, M. Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data. Algorithms 2019, 12, 27.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top