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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.

The automatic identification system (AIS) has become a key element in maritime domains of inquiry and the number of related articles has increased rapidly. The systematic integration of AIS data with other datatypes has received less attention and has mostly resulted in application-specific datasets that are small relative to the available AIS data. This work presents an accumulating multi-purpose database for the northern Baltic Sea that combines nine years of AIS data with marine environmental data. The main application is winter navigation research, for which purpose the environmental data is from ice charts and ice drift models. The AIS data is from terrestrial stations and amounts to 6 billion messages. It has a full update rate which is also required for the analysis of ice navigation as this involves close encounters, icebreaker assistance, convoy operations, and rapid speed changes. To identify and study such traffic features, distances between ships that are close to each other are included in the database. Application examples are given for spatial traffic statistics, reduction of ship speed with increasing ice thickness, and for icebreaker assistance.

Funding Information: The work received funding from SIMREC project (CBC 2014?2020), funded by the European Union, the Russian Federation and the Republic of Finland. The work also received funding from the Lloyd's Register Foundation, a charitable foundation, helping to protect life and property by supporting engineering-related education, public engagement and the application of research www.lrfoundation.org.uk. The contributions by the third author were undertaken in context of the project ?Safe Navigation and Environmental Protection?, thanks in part to funding from the Canada First Research Excellence Fund, through the Ocean Frontier Institute. The authors also thank Fang Li and Mikko Suominen for their valuable comments to the paper. Publisher Copyright: © 2021 The Author(s) Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Ships navigating in ice inevitably encounter different ice conditions. Dynamic ice typically presents severe conditions when it is moving perpendicular towards the parallel midship section, which can lead to ships getting stuck in ice. This can cause delays for ships or even damage to the ship hull. However, there currently is no model to assess ship operability in this dynamic ice. This paper aims to develop a method to assess operability of ships in dynamic ice conditions, which can be used for ship routing to avoid ship stuck. The method is especially useful for emergency response planning purposes, e.g. for marine pollution preparedness and response planning, where an understanding of the operability of response vessels in dynamic ice conditions currently is lacking. First, a transit model is introduced for both independent navigation and escort operations, considering the additional ice resistance by dynamic ice. Then, a ship operability index is proposed based on the modelling of ship's performance. Case studies of independent navigation and escort operations in realistic dynamic ice conditions are investigated to compare with the simulated results. Reasonable agreement is obtained, indicating that the proposed method can be used for ship operability assessment in dynamic ice.

Scientometrics analysis is increasingly applied across scientific domains to gain quantitative insights in the development of research on particular (sub-)domains of scientific inquiry. By visualizing metrics containing quantitative information about such a domain, scientometric mapping allows researchers to gain insights in aspects thereof. Methods have been developed to answer specific research questions, focusing e.g. on collaboration networks, thematic research clusters, historic evolution patterns, and trends in topics addressed. Several articles applying scientometric mapping to safety-related topics have been published. In context of the Special Issue ‘Mapping Safety Science – Reviewing Safety Research’, this article first reviews these, and subsequently provides an overview of key concepts, methods, and tools for scientometric mapping. Data sources and freely available tools are introduced, focusing on which research questions these are suited to answer. A brief tutorial-style description of a scientometrics research process is provided, guiding researchers new to this method how to engage with it. Finally, a discussion on best practices in scientometric mapping research is made, focusing on how to obtain reliable and valid results, and how to use the scientometric maps to gain meaningful insights. It is hoped that this work can advance the application of scientometric research within the safety science community.

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.

Ship performance in level ice involves problems such as attainable speed and maneuverability under different ice thickness. As the alternative to analytical methods, numerical simulation models have been proposed in recent years to improve the accuracy and to analyze more complex operations. However, current numerical models use quite diverse approaches to model the icebreaking components, and focus on rather different aspects, while the choices need more justification. The primary aim of this paper is to propose a framework for the development of effective and efficient numerical models to simulate ship performance in level ice, based on state-of-the-art knowledge of ship-ice interaction. This covers a discussion of modelling purposes and a hierarchical decomposition of the problem, followed by a strategy and two methods which could be applied to improve accuracy and credibility, as well as methodology of validation. Following the proposed framework, a numerical model is proposed as a prototype model, which simulates ship performance in level ice, and can be developed further in future. The numerical simulation results are compared with full-scale measurement results of a ship on the Baltic Sea, indicating reasonable estimation of ship turning. This implies the validity of the model as a prototype of the proposed framework.

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.

Practical knowledge about the performance of a ship while navigating in ice is crucial for the selection of safe and efficient route for a ship. Existing route finding tools estimate ship performance in ice adopting numerous approaches, ranging from model tests and engineering models to experts-based guidelines. Therein ship performance is usually understood as attainable ship speed or the average speed in given ice conditions; rarely the probability of besetting in ice is taken into account. Those models despite being fairly accurate in the theory share the same shortcoming in practice. The latter encompasses three main issues: (1) inaccurate information about prevailing ice conditions, (2) presence of ice conditions that goes beyond the scope of the models, and (3) the effect of operational patterns and traffic organization on the performance of an individual ship. To approach those issues, we propose a hybrid model of ship performance in ice-covered waters. The hybrid model combines two other sub-models: engineering and data-driven. The former determines ship speed and besetting probability in ridged ice field with ice concentration close to 100%. The latter sub-model provides information on ship’s speed in the actual ice conditions, where the speed is affected also by operational restrictions and icebreaker assistance. It is based on an extensive dataset combining ship data from automatic identification system and ice data from ice charts and ice forecast models. The presented hybrid model is valid for a specific ship type, which is ice going bulk carrier (IA Super ice class), operating within the Northern Baltic Sea winter navigation system. The obtained results reveal that the hybrid model in principle is capable of providing reliable information about the performance of a ship in a wide range of conditions accounting for environmental variability and existing operational conditions. The model is suitable for the purpose of safe route planning in ice for a single ship or group of similar ships, accounting for the economy and safety of a voyage.

Even though validation is an important concept in safety research, there is comparatively little empirical research on validating specific safety assessment, assurance, and ensurance activities. Focusing on model-based safety analysis, scant work exists to define approaches to assess a model’s adequacy for its intended use. Rooted in a wider concern for evidence-based safety practices, this paper intends to provide an understanding of the extent of this problem of lack of validation to establish a baseline for future developments. The state of the practice in validation of model-based safety analysis in socio-technical systems is analyzed through an empirical study of relevant published articles in the Safety Science journal spanning a decade (2010–2019). A representative sample is first selected using the PRISMA protocol. Subsequently, various questions concerning validation are answered to gain empirical insights into the extent, trends, and patterns of validation in this literature on model-based safety analysis. The results indicate that no temporal trends are detected in the ratio of articles in which models are validated compared to the total number of papers published. Furthermore, validation has no clear correlation with the specific model type, safety-related concept, different system life cycle stages, industries, or with the countries from which articles originate. Furthermore, a wide variety of terminology for validation is observed in the studied articles. The results suggest that the safety science field concerned with developing and applying models in safety analyses would benefit from an increased focus on validation. Several directions for future work are discussed.

Risk ranking is a widely used technique for selecting and prioritizing multiple risks facing an organization or decision maker. In industrial practice and in academic work, risk ranking methods are commonly based an implicit risk conceptualization, adopting a particular foundational perspective and accounting for selected factors. Nevertheless, different stakeholders may adhere to various risk conceptualizations, applying different foundational perspectives and accounting for different factors in the ranking process. Identifying a gap in the literature to coherently consider these different foundations, this paper presents an approach for reconciling different foundational perspectives and stakeholder views, accounting for uncertainties about these. The approach is presented based on three foundational perspectives (the expected value, uncertainty and moral perspective), and developed as a Bayesian Network – Influence Diagram. The main use of the model is to make possible controversies in risk prioritization explicit, facilitating stakeholder deliberation. The model can easily be extended with new perspectives on risk ranking, or adapted based on additional knowledge about other aspects, e.g. relating to risk perception. The graphical representation allows insights into the contribution of various aspects, facilitating the selection of appropriate risk management actions. The developed model is applied to an example, suggesting that the envisaged benefits of the proposed approach are plausible. It is concluded that the novel approach has a clear potential for reconciling different foundational perspectives and stakeholder views on risk ranking. Nevertheless, it is stressed that the conceptual contribution made in this paper should be more elaborately tested in practical settings, substantiating the findings further.

According to International Maritime Organization (IMO), the Baltic Sea Area has some of the densest maritime traffic in the world. The Baltic Sea was designated a Particularly Sensitive Sea Area (PSSA) at IMO Marine Environment Protection Committee's 53 rd session in July 2005. The Mandatory Ship Reporting System in the Gulf of Finland Traffic Area (GOFREP) was established by IMO in 2003 and has been in operation since 2004. The objective of this study in progress is 1) to outline the hierarchical structure of the maritime navigation safety management system from European to ship onboard level and 2) to apply the Systems-Theoretic Process Analysis (STPA) to identify system level hazards and potentially unsafe ship speed and maneuvering control actions with respect to IMO Collision Regulations (COLREGs) Rules on ship safe speed, ship safe separation distances and the Traffic Separation Schemes requirements with the aim of evaluating the effective hazard control options to enable efficient updating of ship level situational awareness and the enforcement of safety constraints in real time.

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.

Slip and fall incidents at work remain an important class of injury and fatality causing mechanisms. An extensive body of safety research has accumulated on this topic. This article presents an analysis of this research domain. Two bibliometric visualization tools are applied: VOSviewer and HistCite. Samples of 618 slip and fall related articles are obtained from the Web of Science database. Networks of institutions, authors, terms, and chronological citation relationships are established. Collaboration and research activities of the slip and fall research community show that most contributors are from the United States, with the (now closed) Liberty Mutual Research Institute for Safety the most influential research organization. The results of a term clustering analysis show that the slip and fall research can be grouped into three sub-domains: epidemiology, gait/biomechanics, and tribology. Of these, early research focused mainly on tribology, whereas research on gait/biomechanics and epidemiological studies are relatively more recent. Psychological aspects of slip and fall incident occurrence represent a relatively under-investigated research topic, in which future contributions may provide new insights and safety improvements. Better linking of this research domain with other principles and methods in safety science, such as safety management and resilience, may also present valuable future development paths.

Safety for conventional and autonomous navigation in ice-covered waters is a topic of rising importance. Here, we propose a generic extendable framework to provide the optimal route from multiple route planning objectives. These objectives are attained by an evaluation of multi-source input data, including state-of-the-art model data for ice conditions, for bathymetric knowledge, and for ship-ice interaction. Additionally, we model the ship-ship interactions statistically using a mean-field, to account for ships (indirectly) assisting each other via artificial ice channels. For the subsequent pathfinding problem, we propose a new A*-based algorithm that yields output which is not dependent on the grid format of the input data but instead consists of a path that follows the Earth's curvature. The outputs of the algorithm are a set of waypoints (representing the optimal route), the travel costs (expressed in time), and the additional travel cost estimates caused by route deviation, should the optimal route be altered in any way. The steaming speeds, the optimal route, and the deviation times are represented with two-dimensional (2D) maps. Finally, we provide a model implementation of our framework as a Matlab-package, ICEPATHFINDER, that is suitable for both operational and strategic ship route optimization.

openaire: EC/H2020/730888/EU//RESET With the global warming and a large amount of sea ice melting, the available Arctic Sea Route has greatly enhanced the value of Arctic shipping. Ship operations under icebreaker assistance have become an essential way to facilitate the safe navigation of merchant vessels sailing through the Arctic Sea Route in ice-covered waters, but they can also put the crew and the ship in danger caused by a possible collision between the assisted ship and the icebreaker. In this paper, a dedicated Human and Organizational Factors (HoFs) model of ship collision accidents between an assisted ship and an icebreaker is developed and analyzed with the aim to identify and classify collision risk factors. First, a modified model of the Human Factors Analysis and Classification System (HFACS) for collision accidents between a ship and an icebreaker in ice-covered waters is proposed, which helps to analyze ship collision reports. Then, a Fault Tree Analysis (FTA) model is utilized to analyze the fundamental collision risk factors according to the statistical analysis of accident reports and expert judgments based on the HFACS-SIBCI model. Finally, qualitative analysis is carried out to analyze collision risk factors under icebreaker assistance, where Risk Control Options (RCOs) are formulated. An important guidance for the risk control of ship collisions during icebreaker assistance in ice-covered waters is provided for lawmakers and shipping companies.

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.

The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.

In harsh environments, offshore oil and gas support operations are subjected to frequent logistics and supply chain operational disruption, due to harsh environmental factors with their associated risks. To capture these stochastic influential factors and support related decision making, it is helpful to develop a robust and dynamic probabilistic model. The current study presents a proactive methodology that integrates the Pure-Birth Markovian process (PBMP) with the Bayesian network (BN) for the effective analysis of offshore logistics disruption risk. The PBMP captures the stochasticity in the failure characteristics of the engineering systems for estimating the time-evolution degradation probability. The BN explores the dynamic interactions among the most important offshore logistics influential factors to analyze the disruption risk in a harsh environment. The effects of influential factors’ non-linear dependencies are propagated and updated, given evidence on the degree of disruption. The level of logistics disruption is further assessed using cost aggregation-based expectation theory. The theory explores the incurred cost/economic risk under different operational scenarios. The proposed methodology is tested on an offshore supply vessel operation to estimate the likely operational disruption risk in terms of financial loss in a harsh operating environment. The most critical influential functions are assessed to establish their degree of impact on the logistics disruption. At the upper bound probability of disruption occurrence, an economic risk/additional incurred cost of US$2.38E+05 with a variance (𝜎²) of 3.05 × 10⁹ was predicted. The result obtained suggests that the proposed methodology is adaptive and effective for dynamic logistics disruption risk analysis in harsh offshore environments.

Maritime accidents have the potential to cause significant financial loss, injury, and damage to the environment. One approach to investigating maritime safety is to focus on near misses, that is, situations which did not lead to an accident but where an accident was narrowly avoided. Based on the principles of the traffic conflict technique, which ranks traffic encounters through a conflict severity hierarchy, this paper proposes a novel model for screening maritime traffic data for near miss ship-ship encounters, particularly for open sea and coastal restricted sea areas. Compared to previous methods, the proposed method has a greater specificity, leaving fewer possible near miss cases to be assessed by navigational experts in a contextualised traffic setting. This is achieved by including the effect of ship size through a ship domain, and by better accounting for the criticality of the encounter direction through the Minimum Distance To Collision concept compared to earlier proposed models. The factors included in the model and their relation are based on expert judgments and using knowledge from previous studies. Model parameters are derived from AIS data points from a reference encounter situation dataset. The developed model has been applied to traffic data from the Northern Baltic Sea. The model is subjected to a number of validity tests, the results of which suggest that the model is adequate for ranking and prioritizing encounters for further assessment in an expert judgment phase to identify near misses. Thus, it establishes a method to enable subsequent research into the validity of near miss information to make statements of maritime safety in relation to collision accidents.

Estimating the navigational risk of vessels operating in sea and waterway areas is important for waterway risk management and pollution preparedness and response planning. Existing methods relying on a model-informed expert judgment of ship-ship collision risk are of limited practical use because periodic risk monitoring is feasible only when this can be done without extensive use of organizational resources. To alleviate such limitations, this article presents a new approach based Convolutional Neural Networks (CNNs) and image recognition to interpret and classify ship-ship collision risks in encounter scenarios. The specific aim of the article is to investigate whether a CNN-based model can quickly and accurately interpret images constructed based on data from the Automatic Identification System (AIS) in terms of collision risk. To test this, estimates derived from training data are compared to validation data. It is also investigated whether adding additional navigational information based on AIS data improves the model's predictive accuracy. A case study with data from the Baltic Sea area is implemented, where various model design alternatives are tested as a proof-of-concept. The main finding of this work is that a CNN-based approach can indeed meet the specified design requirements, suggesting that this is a fruitful direction for future work. Several issues requiring further research and developed are discussed, with the validity of the risk ratings underlying the image classification seen as the most significant conceptual challenge before a CNN-model can be put to practical use.

Many coastal communities are heavily dependent on maritime transportation for the ingress and egress of people and goods. Any major transportation disruption can have a significant negative impact on the safety, health and wellbeing of affected communities, this is due to the interruption in the availability of food and the supply medicines and fuel. Therefore, preparedness and the forward planning of an effective response are essential for successful emergency and recovery management. Accordingly, in this study, the concept of using AIS (Automatic Identification System) vessel tracking data has been applied for the study of disaster management in coastal communities. The AIS vessel tracking system has been an important development in navigational safety; this is because it continuously transmits important information to all other vessels about a particular vessel (including its position, identity, speed and route). One of the limitations of the AIS tracking system is that AIS data does not indicate commodity specifications: that is, the quantity of essential goods that each vessel is carrying to specified coastal communities. To overcome the limitation of the current AIS tracking system, we use an artificial neural network as an estimation tool. In the current study, AIS data are assessed and analyzed in addition to the augmentation of the capacity information of vessels; thus, the study develops a predictive model so that a relief manager can determine the actual needs of affected residents and thus be able to make responsible relief decisions (e.g., how much relief a disaster-affected community is likely to need). The study makes a unique contribution as its focus seeks to remedy the total lack of research on how to use AIS data in disaster operations.

Maritime transport faces new safety-related challenges resulting from constantly increasing traffic density, along with increasing dimensions of ships. Consequently, the number of new concepts related to Decision Support Systems (DSSs) supporting safe shipborne operations in the presence of reduced ship manning is rapidly growing, both in academia and industry. However, there is a lack of a systematic description of the state-of-the-art in this field. Moreover, there is no comprehensive overview of the level of technology readiness of proposed concepts. Therefore, this paper presents an analysis aiming at (1) increasing the understanding of the structure and contents of the academic field concerned with this topic; (2) determining and mapping scientific networks in this domain; (3) analyzing and visualizing Technology Readiness Level (TRL) of analyzed systems. Bibliometric methods are utilized to depict the domain of onboard DSSs for operations focused on safety ensurance and accident prevention. The scientific literature is reviewed in a systematic way using a comparative analysis of existing tools. The results indicate that there are relatively many developments in selected DSS categories, such as collision avoidance and ship routing. However, even in these categories some issues and gaps still remain, so further improvements are needed. The analysis indicates a relatively low level of technology readiness of tools and concepts presented in academic literature. This signifies a need to move beyond the conceptual stages toward demonstration and validation in realistic, operating environments.

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.

Recently, there have been several calls for increased attention to foundational issues in risk analysis, addressing issues like terminology, principles and theories. An important foundational issue is the appropriateness of different concepts and perspectives for analyzing risk in practical applications. Several authors have addressed this through arguments involving, inter alia, the definition of risk, the ontology of risk, and the reliability and validity of risk analysis. This paper aims to contribute to this discussion by focusing on the concept of prediction. While this term is quite frequently used in risk analysis contexts, no earlier work has specifically focused on the issue of whether risk analyses can be considered to be predictive, and if so, in what sense. Neither has this been linked to the feasibility of risk perspectives. First, two definitions of what prediction can mean are elaborated, and criteria corresponding to these definitions are outlined to facilitate the subsequent discussion. A brief discussion on system types is included, as one type of prediction is defined through the relation between the model and the modeled system. Then, the definitions of prediction and the corresponding criteria are used to consider the appropriateness of two commonly used risk perspectives, namely the probability of frequency and the uncertainty perspective. In the former, a risk analysis aims at estimating an underlying true risk with quantified uncertainty bounds. In the latter, a risk analysis is a descriptive account of judgments and uncertainties by an assessor. It is finally argued that the uncertainty perspective generally is more appropriate than the probability of frequency perspective for practical risk analysis applications.

VK: T20404 In risk assessment of maritime transportation, estimation of accidental oil outflow from tankers is important for assessing environmental impacts. However, there typically is limited data concerning the specific structural design and tank arrangement of ships operating in a given area. Moreover, there is uncertainty about the accident scenarios potentially emerging from ship encounters. This paper proposes a Bayesian network (BN) model for reasoning under uncertainty for the assessment of accidental cargo oil outflow in a ship–ship collision where a product tanker is struck. The BN combines a model linking impact scenarios to damage extent with a model for estimating the tank layouts based on limited information regarding the ship. The methodology for constructing the model is presented and output for two accident scenarios is shown. The discussion elaborates on the issue of model validation, both in terms of the BN and in light of the adopted uncertainty/bias-based risk perspective.

Quantitative risk analysis (QRA) is widely applied in several industries as a tool to improve safety, as part of design, licensing or operational processes. Nevertheless, there is much less academic research on the validity and validation of QRA, despite their importance both for the science of risk analysis and with respect to its practical implication for decision-making and improving system safety. In light of this, this paper presents a review focusing on the validity and validation of QRA in a safety context. Theoretical, methodological and empirical contributions in the scientific literature are reviewed, focusing on three questions. Which theoretical views on validity and validation of QRA can be found? Which features of QRA are useful to validate a particular QRA, and which frameworks are proposed to this effect? What kinds of claims are made about QRA, and what evidence is available for QRA being valid for the stated purposes? A discussion follows the review, focusing on the available evidence for the validity of QRA and the effectiveness of validation methods.

The present paper describes an experiment where a VTS uses the functionality of “route suggestion” to visually support communication between navigators and VTS Operators. The idea of “route suggestion”, a crucial part of current developments within e-navigation, was first introduced with the EfficienSea project and has since been matured within the MONALISA and ACCSEAS projects. Within the ACCSEAS project the functionality has been augmented through the notion of “intended routes”, where vessels transmit sections of their planned routes using ECDIS in order for navigators to better understand the intentions of other, nearby, vessels. Also, “route suggestions” has been developed for use by shore stations, such as VTS, to transmit route segments to individual vessels within its area of responsibility. As part of the ACCSEAS project, the latter was tested in a simulator consisting of two full mission bridges and two VTSs, each of the two working in pairs, bridge-VTS. Five different scenarios were used, all set in the entrance of the river Humber, UK. In total, nine Pilots/Masters (Unlimited licence) and two VTS Operators participated in the experiment which ran over four days and was divided into two different simulations. ECDIS and VTS system were represented by the shipboard as well as the shore-based E-navigation Prototype Display system, as developed by the Danish Maritime Authority within the MONALISA and ACCSEAS projects. Data was collected using questionnaires, observation and video protocols. The observation protocol and the video protocol were coded and micro coded using MaxQDA and the questionnaires were used for comparison between the different participants following the analysis of protocol data in MaxQDA. Results indicate that there were no differences in acceptance of the tested “route suggestion” functionality between participants of different age, nor previous experiences as navigators/VTS Operators. Further, the results strongly indicate that the acceptance of the “route suggestion” functionality was due to the VTS Operators bridging the introduction of the new functionality by voice communication. Thus, the “route suggestion” functionality served as a graphical means of supporting voice communication between navigator and VTS Operator.