FleetMon is currently supporting the research project of Dr. Alexandra Muscalus from the Georgia Institute of Technology (“Georgia Tech”) in Atlanta, Georgia. She uses AIS data from FleetMon to investigate the impact of low-frequency ship wake on shorelines in the far-field.
Over the past two days, FleetMon hosted the second bi-national meeting of our MAREMIS research project here in Rostock. Together with our project partners from IHPC in Singapore, we are developing machine learning-based models and a demonstrator to measure, track, and validate emissions-related aspects of maritime transport to reduce emissions from ships. See what we have achieved so far.
Since FleetMon was founded in 2007, we have supported research and development projects worldwide. We offer students, researchers, and academics access to our comprehensive API suite and our extensive AIS data archive. One of our recent projects is with two research fellows from the University of Aberdeen (Scotland). They examine how natural and man-made environmental changes influence marine mammals and seabirds’ behavior and population dynamics.
Transparency and greener shipping are two of our most important goals at FleetMon. Therefore we support students, universities and institutions in their maritime research projects with AIS data. More than 120 universities are part of our cooperation partners – one is Oxford University. In 2022, Daniel Bundred, a MEng student from the Department of Engineering, contacted us and requested AIS data for his project on decarbonizing global shipping.
FleetMon has a network of over 5,000 AIS receivers to guarantee the best coverage for our customers and partners. Our stations are installed at ports, ships, private buildings, and institutions. The group of AIS partners actively setting up stations worldwide has been built up over the last 15 years at FleetMon.
A new major project from FleetMon’s AIS team is a collaboration with Emily Hague, Ph.D. Researcher from the Marine Spatial Analysis Group at Heriot-Watt University in Scotland. She studies the impact of underwater noise of shipping traffic on marine mammals in UK waters.
FleetMon supports Emily in her project with AIS data, covering the urbanized waterway Firth of Forth over a 5 year period. In return, she helped us to set up more AIS stations around the coast of Scotland.
Visit our Research & Development section to read the original paper published by Ömer Harun Özkernak and Gönül Tuğrul İçemer of the Azdeniz University in Antalya, Turkey.
Bilge water waste poses an environmental risk for humans and marine creatures by causing cancer and developmental disorders due to the toxic substances. This study aims to create a calculation method to calculate the amount of bilge that a ship can produce. The number of ships and the amount of bilge water that they have given the port waste reception facilities in the past years were collected to prevent marine pollution caused by ships in the Gulf of Antalya.
The amount of possible future bilge water discharge in the gulf was estimated by using the collected data by linear regression method. The risk distribution of the amount of bilge water that a ship can produce was determined with the data obtained by the Monte Carlo method for the first time in this study. As a result, although the number of ships in the gulf will decrease in number, it is predicted that the amount of bilge water discharge and the needs of a waste receptions facility will increase in the coming years.
In June 2021, we announced FleetMon’s Innovation Lab, bundling all our Research & Development projects. There’s something new coming out of the Lab:
Around a year ago, we started a pilot project in collaboration with Julius Marine, a local producer of buoys and fairway lighting. The project aims to develop a modular, autonomous AIS station that runs in locations without a power supply or a stable internet connection. In addition to an AIS receiving antenna, the station also contains a variety of measuring sensors. It works autonomously, enabled by two solar panels that supply the powerful battery and the Global System for Mobile Communication (GSM) module for data transmission with energy. This means that the weatherproof station can operate outside all year round to receive ship position data and other measurement data. Servicing the autonomous AIS station is not necessary.
In March 2020, the Technical University Berlin and five representatives of the port industry started the funded research project SELECT. The acronym “SELECT” stands for “Smarte Entscheidungsassistenz für Logistikketten der Binnenschifffahrt durch ETA-Prognosen” (engl.: “Smart decision-making assistance for logistics chains in inland shipping through ETA forecasts”). FleetMon has been chosen as the official AIS data provider.
The aim of the project is to develop an IT system for port operators and shipping companies that automatically and dynamically predicts the transport processes of inland vessels and thus their arrival times (ETA) at inland and seaports. The digital decision assistant is intended to enable the parties to take suitable actions in relation to the expected arrival time. It considers the entire logistical process flow. Reducing the vessel transit/travel times as well as increasing the handling capacities in inland ports are other important goals.
The research partners are supported by the BMVI (Federal Ministry of Transport and Digital Infrastructure) and receive funds amounting to around one million euros.
Additionally, FleetMon’s data have been used for the teaching course “Supply Chain Analytics” at the Technical University Berlin related to the SELECT project. Visit our Research & Development section to know more about the student case studies in the field of inland vessel ETA predictions.
We support students and researchers by offering access to the FleetMon API Suite and our extensive AIS Data Archive with historical vessel position and port call data. Read this guest article we received by Niklas Scherer, a master’s degree student of the University of Applied Sciences in Bingen, Germany.
The academic project investigates a correlation between specific weather conditions a vessel was exposed to and occurring cargo damage. AIS data and weather data were used to examine if certain weather conditions on maritime high-traffic lanes are likely to cause damage to freight in order to prevent damage by realistic forecasting.
Kiel, May 6th, 2021: On Thursday, the Institute for the World Economy presented a new, AI-based leading indicator for international trade based on real-time data from global container shipping. On the basis of up to 250,000 continuously collected data points from up to 200,000 position data and up to 50,000 additional data on inlets and outlets, supplied by FleetMon, the Kiel scientists offer continuous monitoring of imports and exports of the largest economies China, Europe, and the USA.