Publications

Alexandros Troupiotis-Kapeliaris, Giannis Spiliopoulos, Marios Vodas, Dimitrios Zissis

Alexandros Troupiotis-Kapeliaris, Nicolas Zygouras, Manolis Kaliorakis, Spiros Mouzakitis, Giannis Tsapelas, Alexander Artikis, Eva Chondrodima, Yannis Theodoridis, Dimitris Zissis

Konstantina Bereta; Ioannis Karantaidis; Dimitris Zissis,

Leonardo M. Millefiori, Paolo Braca, Dimitris Zissis, Giannis Spiliopoulos, Stefano Marano, Peter K. Willett & Sandro Carniel

Ioannis Kontopoulos; Antonios Makris; Dimitris Zissis; Konstantinos Tserpes, 2021

Spiliopoulos, K Bereta, D Zissis, C Memos, Ch Makris, A Metallinos, Th Karambas, M Chondros, M Emmanouilidou, A Papadimitriou, V Baltikas, Y Kontos, G Klonaris, Y Androulidakis, V Tsoukala

Giannis Spiliopoulos, Dimitris Zissis, Julio de La Cueva, Ioannis Kontopoulos

Konstantina Bereta, Raffaele Grasso & Dimitris Zissis

Antonios Makris, Konstantinos Tserpes, Giannis Spiliopoulos, Dimitrios Zissis & Dimosthenis Anagnostopoulos

Ioannis Kontopoulos, Iraklis Varlamis and Konstantinos Tserpes

Konstantina Bereta, Raffaele Grasso & Dimitris Zissis

Konstantina Bereta, Raffaele Grasso & Dimitris Zissis

Ioannis Kontopoulos; Konstantinos Chatzikokolakis; Dimitris Zissis; Konstantinos Tserpes; Giannis Spiliopoulos

Ioannis Kontopoulos, Konstantinos Chatzikokolakis, Konstantinos Tserpes, and Dimitris Zissis

A. Milios, K. Bereta, K. Chatzikokolakis, D. Zissis and S. Matwin

D. Zissis, K. Chatzikokolakis, G. Spiliopoulos and M. Vodas

Zissis D. , Chatzikokolakis K. , Vodas M. , Spiliopoulos G. and Bereta K.

Konstantina Bereta, Aristides Millios, Konstantinos Chatzikokolakis, Dimitris Zissis

Improving Maritime Situational Awareness Through Big Data Analytics, Machine Learning and Artificial Intelligence

K.Chatzikokolakis, D.Zissis,M.Vodas, G.Spiliopoulos,I.Kontopoulos

Mannarini, Gianandrea; Carelli, Lorenzo; Zissis, Dimitris; Spiliopoulos, Giannis; Chatzikokolakis, Konstantinos

Konstantinos Chatzikokolakis, Dimitrios Zissis, Giannis Spiliopoulos,Konstantinos Tserpes

Konstantinos Chatzikokolakis, Dimitrios Zissis, Marios Vodas, Giannis Tsapelas, Spiros Mouzakitis, Panagiotis Kokkinakos, Dimitris Askounis,

Konstantinos Chatzikokolakis, Dimitrios Zissis, Giannis Spiliopoulos

Elias Xidias and Dimitris Zissis

Ioannis Kontopoulos, Giannis Spiliopoulos, Dimitrios Zissis, Konstantinos Chatzikokolakis, Alexander Artikis

Konstantinos Chatzikokolakis, Dimitrios Zissis, Giannis Spiliopoulos and Konstantinos Tserpes

FleetMon provided inland AIS data for the SELECT project of the TU Berlin. This article, published in the journal Internationales Verkehrswesen, features how data-based arrival time forecasts can increase the reliability of inland waterway transports.

The evaluation of a (maritime) traffic situation requires sound training and professional experience. Decisions can be made based on this training and experience. (Partially) autonomous ships must be trained or require generalized algorithms to react appropriately in any situation. The goal is for vessels to be able to determine the technical manoeuvring distance and the required personal perceived safety distance.

FleetMon supported researchers at the Akdeniz University in Antalya, Turkey and their study on bilge water waste risk in the Gulf of Antalya.

Abstract: Fighting climate change demands action in all sectors. International shipping faces the challenge of long lifetimes of vessels compared to other modes of transportation like cars or aircraft. Decisions on energy carriers and propulsion technologies that are made now have a long-lasting impact on the emissions of the sector.

Abstract: Shore-side electricity can drastically reduce the emissions from fossil fuel-powered auxiliary engines of ships at berth. Data scarcity on the auxiliary power demand at berth has limited the scope and temporal resolution of previous studies to few ports and ships.

The reduction of emissions is one of the main common goals all over the globe.

Authors: Dennis Marten, Carsten Hilgenfeld, Andreas Heuer

Globalization and new environmental legislations lead to a rising need for new technological developments for the shipping industry

As part of the PRESEA research project, which began in summer 2019, a real-time-based sea traffic forecast application is to be

Abstract: For the international exchange of goods, an exact estimated time of arrival (ETA), especially in case of delays, is of great importance. Using global data of the automatic identification system (AIS) a grid node is generated. The sum of such nodes and their connections form a routing graph. As an example, with one node of in total more than 100,000 nodes it is described how this point gets the maximum vessel length and draft assigned.

The composition, spatial distribution and source of marine litter in the Spanish Southeast Mediterranean were assessed. The data proceed from a marine litter retention programme implemented by commercial trawlers and were analysed by GIS.

Anticipated changes in the global ocean climate will affect the vulnerability of marine ecosystems to the negative effects of non-indigenous species (NIS)

In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS).

Digital twins are increasingly valuable in sectors like maritime,energy, logistics and transportation. In the maritime industry, the complexity of monitoring vessel traffic, necessitates more sophisticated, data-driven approaches due to the high volume of vessels and intricate movement patterns. This paper introduces GMSA, adigital twin application for maritime route and event forecasting for the entire global fleet, utilizing the real-time AIS streaming service of Kpler (MarineTraffic), for maritime event detection, vessel route prediction and traffic state estimation. Through the combined views of the real-time event detection functions, the vessel and port-specific data driven models, and the visualization of historical aggregated vessel mobility metrics, the application creates a a multi-layer information system for efficient, proactive action planning and enhanced decision making for the end-user.

Digital twins serve as virtual representations of physical environments that are increasingly valuable across various sectors, including maritime operations. The complexity of monitoring vessel traffic through the Automatic Identification System (AIS), demands more sophisticated, data-driven approaches due to the extreme vessel volume and intricate vessel movement patterns.

More than 70% [22] of the global trade transportation is conducted by sea, through maritime sea lanes. Unlike the well defined global land transportation network that consists of roads and railways, the maritime equivalent consists of port connections and is vaguely defined by marine charts’ guidelines, constraints and common sea routes. By definition, the port of origin and port of destination are well defined locations. However, the routes that the vessels follow in between are not strictly defined. Local conditions, such as the weather or traffic congestion, vessel-specific characteristics or other external conditions also affect the route choice and planning.