Digitization in international shipping is an increasingly important topic, but for many years, the lack of accepted international standards and the usage of many different regional solutions, especially for communication between ships and ports, has made the introduction of digitalized solutions difficult. Since 2020, important work has been done in IMO to harmonize international standards supporting ship-port interactions, and this work has now been supported by both shipping, ports, and international standardizations organizations. IMO, through its facilitation committee (FAL) and EGDH (Expert Group on Data Harmonization) is developing the IMO Reference Data Model that covers mandatory reporting requirements related to port calls. This conceptual data model is mapped to three technical data models in three different domains, namely, UNECE (trade), WCO (customs) and ISO 28005 (maritime) to ensure the interoperability between the different ICT systems participating in the data exchange. The IMO Reference Data Model has also been extended with operational data to handle Just-In-Time arrival and departure and also nautical information to ensure that the specification of the locations in ports (berths, pilot boarding places, bollards etc) are the same for different usages. Several international organizations as BIMCO (the largest ship owners' organization) and international port organisations as IAPH, IPCSA and IHMA are strongly involved in this work. This paper summarizes work done by IMO and others to clarify the roles, functionalities and ICT-systems (Information and Communications Technology) that are needed to support the various processes needed to be performed during a port call. These definitions will form the basis for defining a Maritime ITS (Intelligent Transport System) Architecture which will also need to be related to road ITS and also to e-Navigation functionalities. The Maritime ITS Architecture described in this paper contains three levels, namely the Domain Definition (generalized roles that represent people, organizations and equipment in the system), the Processes (definitions of processes and functions that need to be supported to make the domain work), and the Information model (a generalized information model covering the information elements that are required by the functions and processes). In addition to this comes the layers to describe the physical implementation architecture, and the layers to describe the service implementation (e.g. APIs) and the protocols
The maritime industry is undergoing a rapid evolution through the introduction of new technology and the digitization of existing services. At the same time, the digital attack surface is increasing, and incidents can lead to severe consequences. This study analyses and gives an overview of 46 maritime cyber security incidents from the last decade (2010-2020). We have collected information from open publications and reports, as well as anonymized data from insurance claims. Each incident is linked to a taxonomy of attack points related to onboard or off-ship systems, and the characteristics have been used to create a Top-10 list of maritime cyber threats. The results show that the maritime sector typically has incidents with low frequency and high impact, which makes them hard to predict and prepare for. We also infer that different types of attackers use a variety of attack points and techniques, hence there is no single solution to this problem.
A paradigm shift is presently underway in the shipping industry promising safer, greener and more efficient ship traffic. In this article, we will look at some of the accidents from conventional shipping and see if they could have been avoided with autonomous ship technology. A hypothesis of increased safety is often brought forward, and we know from various studies that the number of maritime accidents that involves what is called “human error” ranges from some 60‐90 percent. If we replace the human with automation, can we then reduce the number of accidents? On the other hand, is there a possibility for new types of accidents to appear? What about the accidents that are today averted by the crew? This paper will present a method to assess these different aspects of the risk scenarios in light of the specific capabilities and constraints of autonomous ships.
Automatic Identification System (AIS) is primarily used as a tracking system for ships, but with the launch of satellites to collect these data, new and previously untested possibilities are emerging. This paper presents the development of heuristics for establishing the specific ship type using information retrieved from AIS data alone. These heuristics expand the possibilities of AIS data, as the specific ship type is vital for several transportation research cases, such as emission analyses of ship traffic and studies on slow steaming. The presented method for developing heuristics can be used for a wider range of vessels. These heuristics may form the basis of large-scale studies on ship traffic using AIS data when it is not feasible or desirable to use commercial ship data registers.
The MUNIN project is doing a feasibility study on an unmanned bulk carrier on an intercontinental voyage. To develop the technical and operational concepts, MUNIN has used a risk-based design method, based on the Formal Safety Analysis method which is also recommended by the International Mari-time Organization. Scenario analysis has been used to identify risks and to simplify operational scope. Systematic hazard identification has been used to find critical safety and security risks and how to address these. Technology and operational concept testing is using a hypothesis-based test method, where the hypotheses have been created as a result of the risk assessment. Finally, the cost-benefit assessment will also use results from the risk assessment. This paper describes the risk assessment method, some of the most important results and also describes how the results have been or will be used in the different parts of the project.
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