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.
This project aims to increase maritime safety and is designed in the area of safety research. Through the use of artificial intelligence, the Vessel Traffic Service Center (VTS), Fleet Operation Center (FOC), and Vessel Coordination Center (VCC) will be technologically prepared for the occurrence of highly automated vessels. Concepts are being developed to visualize this information in the Human Machine Interface (HMI).
A method is being developed for sea areas and ports to determine the number of people at sea. For this purpose, information from the FleetMon ship databases and community is bundled. The number of persons in a maritime area is essential for the provision of sufficient maritime rescue resources.
In the project, a method is being developed to identify near-accidents of ships. For this purpose, the technical limits of the ships but also the personal safety area of the nautical officers will be examined. These can then be used for the development of new guidelines for vessel operations.
FleetMon is involved in a variety of research & development projects. Learn more about our latest projects.
