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Advanced maritime operations, such as remote pilotage, are vulnerable to new emergent risks due to increased system complexity and a multitude of interactions. Thus, maritime researchers this decade have combined Systems-Theoretic Process Analysis (STPA) and Bayesian Network (BN) to effectively manage these risks. Although these methods are effective in identifying hazards and analyzing risk levels, none of the STPA-BN studies provides a systematic process for selecting a cost-effective combination of risk control measures. Cost-benefit analysis is crucial for organizations to make informed risk-based decisions in allocating available resources for risk mitigation and achieve a balance between risk reduction (benefits) and costs associated with risk control measures. This study offers an innovative method of integrating the STPA-BN-Influence diagram for risk-based decision-making through a cost-benefit analysis. The model automatically evaluates the costs and benefits of all possible risk control options and proposes the optimum cost-effective solution. In the current study, the methodology is illustrated with a case study of remote pilotage operation, where 524,288 different risk control options (combinations of 19 risk control measures) are assessed to select an optimal risk control option. The case study results indicate that the proposed methodology is more significant when the number of risk control measures increases.

While the concept of smart shipping is expected to shape the future of the maritime industry, its safety is still a major concern. New risks might emerge when shifting from human controllers onboard, to autonomous software controllers and remote human controllers. The uncertainties associated with the emerging risks require an efficient decision-making methodology to ensure ship safety. This paper proposes a framework for selecting Risk Control Options (RCOs) of ships with higher degrees of autonomy in the context of marine risk assessment and Formal Safety Assessment (FSA). The framework uses the System Theoretic Process Analysis (STPA) for the hazard analysis and the identification of RCOs, while Bayesian Network is employed in the framework for estimating the system risk. Integrating STPA and BN offers the possibility to cover most of the steps of both risk assessment and FSA and permits the prioritization of the identified RCOs. The proposed method is applied to a concept of an autonomous seawater coolingsystem (SWC) as an illustrative case study. The results indicate that the RCOs including sensors health monitoring and software testing should be prioritized to reduce the risk. This is unveiled by the STPA analysis which shows the risk contribution of the associated causal scenarios.

The timing of a ship taking evasive maneuvers is crucial for the success of collision avoidance, which is affected by the perceived risk by the navigator. Therefore, we propose a collision alert system (CAS) based on the perceived risk by the navigator to trigger a ship’s evasive maneuvers in a timely manner to avoid close-quarters situations. The available maneuvering margins (AMM) with ship stability guarantees are selected as a proxy to reflect the perceived risk of a navigator; hence, the proposed CAS is referred to as an AMM-based CAS. Considering the dynamic nature of ship operations, the non-linear velocity obstacle method is utilized to identify the presence of collision risk to further activate this AMM-based CAS. The AMM of a ship are measured based on ship maneuverability and stability models, and the degree to which they violate the risk-perception-based ship domain determines the level of collision alert. Several typical encounter scenarios are selected from AIS data to demonstrate the feasibility of this AMM-based CAS. The promising results suggest that this proposed AMM-based CAS is applicable in both ship pair encounter and multi-vessel encounter scenarios. Collision risk can be accurately detected, and then a collision alert consistent with the risk severity is issued. This proposed AMM-based CAS has the potential to assist autonomous ships in understanding the risk level of the encounter situation and determining the timing for evasive maneuvers. The advantages and limitation of this proposed method are discussed.

Over the next few years, digitalization and automation are expected to be key drivers for maritime transport innovation to be key drivers for maritime transportation innovation. This revolutionary shift in the shipping industry will heavily impact the reliability of the machinery which is intended to be operated remotely with minimum support from humans. Despite a large amount of research into autonomous navigation and control systems in maritime transportation, the evaluation of unattended engine rooms has received very little attention. For autonomous vessels to be effective during their unmanned mission, it is essential for the engine room understand its health condition and self-manage performance. The unattended machinery plant (UMP) should be resilient enough to have the ability to survive and recover from unexpected perturbations, disruptions, and operational degradations. Otherwise, the system may require unplanned maintenance or the operation will stop. Therefore, the UMP must continue its operation without human intervention and safely return the ship to port. This paper aims to develop a machine learning-based model to predict an UMP's performance and estimate how long the engine room can operate without human assistance. A Random Process Tree is used to model failures in the unattended components, while a Hierarchical Bayesian Inference is adopted to facilitate the prediction of unknown parameters in the process. A probabilistic Bayesian Network developed and evaluated the dependent relationship between active and standby components to assess the effect of redundant units in the performance of unattended machinery. The present framework will provide helpful additional information to evaluate the associate uncertainties and predict the untoward events that put the engine room at risk. The results highlight the model's ability to predict the UMP's trusted operation period and evaluate an unattended engine room's resilience. A real case study of a merchant vessel used for short sea shipping in European waters is considered to demonstrate the model's application.

The maritime industry is currently ongoing into a digital transformation to develop cleaner, safer and smarter transport services. Establishing such services requires identifying and assessing new emerging risks such as software and design flaws. Thus, suitable hazard identification and risk analysis methods must be developed and implemented for these complex services. This study aims to develop a novel risk analysis methodology by integrating Systems Theoretic Process Analysis, Bayesian Network, Noisy-OR gates, Parent divorcing technique and Sub-modelling. The effectiveness of the proposed methodology is demonstrated through a case study of Remote pilotage operation. The results show that the methodology can be applied to complex operations to assess the propagation of risks from a single fault or failure in a system to the hazards, accidents and incidents, and ultimately the losses. Furthermore, it is demonstrated how the remote pilotage risk model can support pilots and pilotage companies in real-time decision-making by estimating the likelihood of losses in case of a single fault or failure.

Towing operations are highly reliant on the experience of the towing operators. Safety concerns arise when towing operations are subjected to environmental disturbances and dynamic traffic conditions. However, a systematic framework and approaches to enhance the safety and automation of towing operations remain lacking. This work proposes a framework of collision prevention of ship towing operations under environmental disturbance in near port waters. The focus is to prevent internal collisions between tug and assisted ship and provide early warning of possible collisions with other surrounding ships. A cooperative multi-agent control strategy is employed to specify the direction and magnitude of the towing force of the two tugs in real-time. Therefore, in the presence of environmental disturbance, the assisted ship can sail along the planned trajectory, and the acceptable safe geometric distance between each ship pair in the towing system is guaranteed. Further, a COLREGs-compliant collision alert system is designed to promptly remind the towing operators of a collision hazard with nearby ships, and different alert levels indicate different action obligations of towing operators. This proposed framework and developed methods are applied to a tandem towing system consisting of two tugs and one assisted ship to test its feasibility.

Funding Information: The first author of this work is supported by the China Scholarship Council (Grant Number: 201606950009) and Marine Technology research group in Aalto University (9170094). This work is also supported by the National Science Foundation of China (NSFC) through Grant No. 52001237 and 52001241. The work has further received financial support from the Canada First Research Excellence Fund, through the Ocean Frontier Institute. This financial support is gratefully acknowledged. Besides, we thank the two anonymous reviewers for their very insightful comments, which have been very instrumental to improve an earlier version of this work. Publisher Copyright: © 2021 This paper introduced a new ship domain concept and an analytical framework. The ship domain takes the point of the ship’s first evasive maneuver as a basis and correlates it with the navigator-perceived collision risk level. The first evasive maneuver of a ship is detected based on the ship turning point identification and ship intention estimation. The available maneuvering margin (AMM) is utilized as a proxy to measure the perceived collision risk by the navigator. Interpreting the first evasive maneuver in terms of this AMM over a large sample of vessel encounters taken from automatic identification system (AIS) data finally enables an empirical estimation of the size of this ship domain. The method is applied to AIS data in the Northern Baltic Sea, and separate ship domains are constructed for the give-way and stand-on vessels with different maneuverability characteristics. Compared to the existing proximity-based ship domain, this ship domain explicitly incorporates the dynamic nature of the encounter process and the navigator’s evasive maneuvers. Several advantages of this proposed ship domain concept and limitations of the presented modeling approach are discussed. Finally, possible future applications are explained, including waterway safety assessment and navigational decision support systems to reduce ship-ship collision risk.

Highlights • A new risk management framework for maritime Pollution Preparedness and Response is proposed. • The framework aims to comply with the complex reality of Pollution Preparedness and Response risk management. • The framework is based on the current scientific knowledge and practical needs of its end-users. Several risk management frameworks have been introduced in the literature for maritime Pollution Preparedness and Response (PPR). However, in light of the actual needs of the competent authorities, there is still a lack of framework that is established on a sound risk conceptual basis, addresses the different risk management decision-making contexts of organizations, and provides tools for various risk management questions of this field. To alleviate the limits of existing approaches, this paper introduces a new risk management framework for this purpose, which was developed in cooperation with the competent authorities and other maritime experts. The framework adopts the risk-informed decision-making strategy and includes three aligned components. The first component provides a unified theoretical risk concept to the framework through an interpretation of the Society for Risk Analysis risk approach. The second consists of four ISO 31000:2018 standard based processes focused on different risk management decision-making contexts of the PPR organizations. The third comprises a set of practical risk assessment tools to generate the needed information. A case study provides an example of the functionality of this framework with integrated data from the northern Baltic Sea. To conclude, a risk concept is provided for the PPR authorities and their stakeholders as well as processes for managing the risk and tools for its assessment.

This paper aims to identify the gap between cruise passengers’ expectations and the on-board experience (referred to in the text as ‘the on-board experience gap’) in the context of on-board environmental factors and overall satisfaction. It also investigates the relationship between cruise passengers’ pre-cruise expectations and their post-cruise experience. The study adopts a comparative approach that examines three classes of cruise ships through open-source data (N = 2649). The aim of the comparison was to understand what creates a gap between cruise passengers’ expectations and the on-board experience, and to explain why some cruise ships are more successful than others. The result of this paper concludes that some of the cruise experiences are not aligned with the customers’ expectations, and the on-board experience gap is also clearly observed between the elements. This on-board experience gap concept can be used to provide a dynamic procedure to measure the customers’ cruise experience.

openaire: EC/H2020/730888/EU//RESET Safety assurance of autonomous ships is one of the major long-term challenges faced by the maritime world. Applying systemic hazard analysis methods at this early stage will guide the design and operation of safe autonomous ships. This paper proposes an initial hierarchical ship systems structure that could be the basis for a systemic hazard analysis of autonomous ship systems and operations. The approach is based on the systems theory and the principle of hierarchy and has been developed via the combination of models used in past research projects and requirements of the STCW convention. For enabling the operation of autonomous ships, the ship crew functions are either replaced by ship technical systems or assigned to the Shore-Based Control Centre (SCC).

openaire: EC/H2020/730888/EU//RESET As autonomous ships are currently developed, modern technologies are implemented into ship systems for enabling autonomous operations. Tight coupling in safety-critical systems created new challenges for the engineers and operators. Designing, operating and analyzing these complex systems requires a deep understanding about the system composition, requirements and expected behavior or functionality. The increasing complexity of the systems requires the implementation of modern model-based approaches. Instead of large texts, these new modelling techniques aim to present detailed system information with simplified models. This paper compares system modelling techniques known as System Modelling Language (SysML) and Object Process Methodology (OPM). These methods are used to model a Dynamic Positioning system (DP-system). Results show that the SysML is more suitable than OPM for modelling the autonomous ship systems due to its ability to present detailed system information in a simple and coherent way.

Designing maritime safety management systems commonly follows basic processes which focus on fulfilling the demands of the regulations in the industry. This provokes designing systems with limited application which are not capable to efficiently use the guidance contained in regulatory demands, and more importantly, creating systems which are not capable of representing, evaluating, and improving the dynamic management of safety-critical organizations. This article proposes a safety system engineering process for designing maritime safety management systems which is based on the Systems-Theoretic Accident Modelling and Processes (STAMP). This process is applied for sketching the safety management of the Vessel Traffic Services in Finland. The aim is to systematically represent the function of the utilized controls for ensuring the internal VTS safety management and the safety of navigation in Finnish sea areas. The outcome of this study provides a descriptive process of analysis for designing maritime safety management systems. In this process, two other concrete elements are included for supporting the functioning of the safety management system to be designed. First, the adaptation of an identification process for determining key performance indicators for planning, monitoring and evaluating the functioning of the safety management system. Second, the constitution of a performance monitoring tool capable of executing the monitoring, measuring, and updating of the determined key performance indicators and the general functioning of the designed safety management system.

This article investigates how the-organization of the shipbuilding process influences production efficiency-by creating a model of the shipbuilding process utilizing a Bayesian network. In this model, the variables are determined by factors having an impact on production efficiency. Causal dependencies between these variables create the network. Three different configurations of organization are included, namely, line organization, project organization, and a hybrid organization. All model probabilities for reaching the targeted production efficiency are elicited from experts. Based on the results, project organization has the highest probability of achieving the desired efficiency. The higher probability is mainly because, in project organization, clear responsibilities lead to target-oriented problem solving and decision making. The model demonstrates that organization has impact on production efficiency. The model can also be used for further developing the cruise shipbuilding process.

Navigational accidents in wintertime conditions occur relatively frequently, yet there is little systematic knowledge available about the circumstances under which these occur. This paper presents an analysis of navigational shipping accidents in the Northern Baltic Sea area which occurred in the period 2007–2013. The analysis is based on an integration of various data sources, aiming to reconstruct the accident conditions based on the best available data sources. Apart from basic accident information from the original accident databases, data from the Automatic Identification System is used to obtain insight in the operation type during which the accident occurred, as well as into other dynamic aspects of the accident scenario. Finally, atmospheric and sea ice data is used to reconstruct the navigational conditions under which the accidents occurred. The analysis aims to provide qualitative insights in patterns and outlier cases in the accidental conditions. Correspondingly, visual data mining is selected asanalysis approach, because of its utility in obtaining qualitative knowledge from data sources through a combination of visualization techniques and human interaction with the data. Special attention is given to the strength of evidence of the identified accident patterns. The results are primarily useful for improving risk analyses focusing on oil spill risks in winter conditions and for developing realistic training scenarios for oil spill response operations.

Autonomous vessels have become a topic of high interest for the maritime transport industry. Recent progress in the development of technologies enabling autonomous systems has fostered the idea that autonomous vessels will soon be a reality. However, before the first autonomous vessel can be released into her actual context of operation, it is necessary to ensure that it is safe. This is a major challenge as the experience of autonomous ships is very limited. This study highlights the need for elaborating a systemic and systematic hazard analysis since the earliest design phase of an autonomous vessel. In particular, it proposes a process for elaborating an initial hazard analysis and management that provides coherent, transparent and traceable safety input information for the design of an autonomous vessel. The process is applied to analyse the hazards of two autonomous vessel concepts for urban transport in the city of Turku, Finland.

openaire: EC/H2020/730888/EU//RESET A safety management system (SMS) is the common means used by organizations to assess organizational performance with respect to the safety and well-being of people, property and the natural ecosystem. A SMS provides confidence to diverse stakeholders that organizational safety is at an appropriate level and fulfils the applicable regulatory standards. As a multifaceted system for organizational safety assessment, ensurance and assurance, the evaluation of the design and operational use of SMS is a complex process. An evaluation needs to provide evidence about how well the design and operation of an SMS complies with applicable standards and how well the methods used in the SMS implementation support the organizational policies and practical work. In the maritime domain, SMS is broadly applied. However, there are few theoretically rooted SMS design approaches, and there is a lack of frameworks to evaluate how well the SMS is designed and how effectively it operates. This paper proposes an initial evaluation framework for the design and operational use of a maritime SMS design approach based on Systems-Theoretic Accident Model and Processes (STAMP), realist evaluation and Bayesian Networks. This framework is applied for a case study of vessel traffic services (VTS) Finland to test its relevance and ability to guide the SMS design. The experiences gained in the case study, and the related discussion on the framework, can guide further research in this area. Ultimately, the work can be used as a basis for developing maritime SMS auditing processes, based on specific theoretical and methodological approaches.

Maritime piloting, the process of navigating ships safely in congested waters, is an area of maritime operations where emerging technologies may be used to develop and implement more robust navigation safety systems. This is supported by the commercial potential of remote piloting as a substitute for pilotage exemptions, today granted to experienced captains. This paper introduces a novel configuration for remote piloting operations in intelligent fairways. Results are supported by methodological and empirical research with the aim to enhance our understanding of merging the role of a pilot in autonomous fairway infrastructures. It is demonstrated that under controlled conditions remote piloting may be a safe option in selected fairways. However, upscaling will require greater understanding of risks associated with the implementation of emerging technologies , fairway features and environmental phenomena.

openaire: EC/H2020/730888/EU//RESET The lack of situational awareness is a major cause of ship collisions. Thus, enhancing the situational awareness of the stand-on ship is a key for navigational safety, where the intention estimation of the give-way ship is crucial. According to COLREGs, the stand-on ship is not allowed to take evasive actions until the give-way ship does not take proper actions timely. The stage that needs the stand-on ship to take actions plays as the second protective layer for the ship, which is named as ‘Stand-on Ship as Second Line of Defense’ (SLoD). A method to estimate the intention of the give-way ship and to trigger SLoD is proposed in this article. Four modules of the proposed method include: “data pre-processing” collects all traffic information and determines the ships' obligations; “action identification” pinpoints the turning points; “action uncertainty” generates a bounded reachable velocity considering the give-way ship's maneuverability; “conflict assessment” judges potential collision by using non-linear velocity obstacle algorithm. Several typical encounter scenarios are simulated to demonstrate the feasibility of the proposed method. The results show that intention estimation of the give-way ship improves the situational awareness of the stand-on ship, which can support the stand-on ship to make collision avoidance decisions.

The Northern Baltic Sea, as one of the few areas with busy ship traffic in ice-covered waters, is a typical sea area exposed to risk of ship accidents and oil spills in ice conditions. Therefore, oil spill capability for response and recovery in this area is required to reduce potential oil spill effects. Currently, there are no integrated, scenario-based models for oil spill response and recovery in ice conditions. This paper presents a Bayesian Network (BN) model for assessing oil spill recovery effectiveness, focusing on mechanical recovery. It aims to generate holistic understanding and insights about the oil spill-to-recovery phase, and to estimate oil recovery effectiveness in representative winter conditions. A number of test scenarios are shown and compared to get insight into the impact resulting from different oil types, spill sizes and winter conditions. The strength of evidence of the model is assessed in line with the adopted risk perspective.

Winter navigation is a complex but common operation in north-European sea areas. In Finnish waters, the smooth flow of maritime traffic and safety of vessel navigation during the winter period are managed through the Finnish–Swedish winter navigation system (FSWNS). This article focuses on accident risks in winter navigation operations, beginning with a brief outline of the FSWNS. The study analyses a hazard identification model of winter navigation and reviews accident data extracted from four winter periods. These are adopted as a basis for visualizing the risks in winter navigation operations. The results reveal that experts consider ship independent navigation in ice conditions the most complex navigational operation, which is confirmed by accident data analysis showing that the operation constitutes the type of navigation with the highest number of accidents reported. The severity of the accidents during winter navigation is mainly categorized as less serious. Collision is the most typical accident in ice navigation and general cargo the type of vessel most frequently involved in these accidents. Consolidated ice, ice ridges and ice thickness between 15 and 40 cm represent the most common ice conditions in which accidents occur. Thus, the analysis presented in this article establishes the key elements for identifying the operation types which would benefit most from further safety engineering and safety or risk management development.

This paper presents a model of maritime safety management and its subareas. Furthermore, the paper links the safety management to the maritime traffic safety indicated by accident involvement, incidents reported by Vessel Traffic Service and the results from Port State Control inspections. Bayesian belief networks are applied as the modeling technique and the model parameters are based on expert elicitation and learning from historical data. The results from this new application domain of a Bayesian network based expert system suggest that, although several its subareas are functioning properly, the current status of the safety management on vessels navigating in the Finnish waters has room for improvement; the probability of zero poor safety management subareas is only 0.13. Furthermore, according to the model a good IT system for the safety management is the strongest safety-management related signal of an adequate overall safety management level. If no deficiencies have been discovered during a Port State Control inspection, the adequacy of the safety management is almost twice as probable as without knowledge on the inspection history. The resulted model could be applied to performing several safety management related queries and it thus provides support for maritime safety related decision making.

The wintertime maritime traffic operations in the Gulf of Finland are managed through the Finnish-Swedish Winter Navigation System. This establishes the requirements and limitations for the vessels navigating when ice covers this area. During winter navigation in the Gulf of Finland, the largest risk stems from accidental ship collisions which may also trigger oil spills. In this article, a model for managing the risk of winter navigation operations is presented. The model analyses the probability of oil spills derived from collisions involving oil tanker vessels and other vessel types. The model structure is based on the steps provided in the Formal Safety Assessment (FSA) by the International Maritime Organization (IMO) and adapted into a Bayesian Network model. The results indicate that ship independent navigation and convoys are the operations with higher probability of oil spills. Minor spills are most probable, while major oil spills found very unlikely but possible.

The maritime transportation industry currently employs several mandatory and non-mandatory norms of organizational safety management. These safety norms are commonly included in integrated maritime safety management systems, which aim at developing, monitoring, controlling and improving the safety of all related shipping operations. These systems are typically evaluated by following key performance indicators, which enable defined measures for various safety management components. However, the identification of indicators addressing safety management requirements constitutes a complex and generally unsystematic process for safety managers in the maritime industry. This article proposes a new method to assess the guidelines available in maritime safety management norms. The proposed method is applied to assess the content of two maritime safety management norms. The aim of this assessment is to identify a set of maritime safety management indicators that can systematically measure the most relevant components of maritime safety management. The application of this method resulted in the identification of 53 key performance indicators for monitoring and reviewing 23 identified safety management components that are commonly integrated into the functioning of maritime safety management systems. The method proposed provides guidance to accurately capture the actual aim and function of the key performance indicators. Furthermore, the indicators and safety components obtained with this method can be adopted as the basis for a safety management system and/or for the analysis of a safety management system already established in the industry.