Datasets of disrupted transportation networks on Canada's West Coast in a plausible M9.0 Cascadia Subduction Zone earthquake scenario.

This article presents a database with geographical and demographic information characterizing the impacts to road and maritime networks, and coastal communities, of a plausible magnitude M9.0 megathrust Cascadia Subduction Zone earthquake scenario near Vancouver Island in British Columbia, Canada. The database consists of a medium and a high impact case associated with the earthquake scenario. The data include the geographical location of communities, ports, and airports/helipads/heliports, the structure of the roads network and their expected damage levels, the resilience level and population size of the communities on Vancouver Island, and the trajectories, expected delays and capacities of ferries and barges. The data originates from government and carriers' open available reports and external datasets, and several impact models. The primary purpose of this database is to support disaster management researchers working to develop and test network models that focus on road repair and restoration, and on the multi-modal distribution of relief supplies to victims. In addition, the data can be used to test heuristic and metaheuristic approaches applied to network models in the context of natural disasters.

Benchmark instances for road network repair and restoration problems in the context of disaster response operations.

This article presents a database which contains a comprehensive and systematically varied set of network instances. These can be applied as benchmarks for multiple road repair and restoration problems in the context of natural disasters. The characteristics of the instances vary in terms of network size, intensity and type of disaster affecting the road network, the epicenter's location, and the number of sub-networks in which the initial network is divided after the disaster occurs. The instances were developed primarily for the Multi-vehicle Prize Collecting Arc Routing for Connectivity Problem (KPC-ARCP). These are however easily adaptable to other well-known connectivity, vehicle routing, and facility location problems in the Operations Research literature. The instances are available on a public repository, as is the Python code to generate the instances.

Bayesian inference modeling to rank response technologies in arctic marine oil spills.

Marine oil spills have a detrimental effect on aquatic systems. Yet, it is challenging to select appropriate technologies in the Arctic because of limited logistics support, inclement weather conditions, and remoteness, and limited research has been conducted in this direction. This article suggests a method to rank the oil response technologies, including mechanical recovery, chemical dispersant, and in-situ burning, for use in Arctic oil spill risk assessment and preparedness planning. The proposed Preference Learning based Bayesian Inference Modeling offers data-driven ranking of systems by learning a label function and considers factors such as ice covered sea areas, cold weather, and spill volume. A data generation system is developed to produce numerous oil spill scenarios, using a state-of-the-art engineering tool. Results demonstrate that the model, while simple, can efficiently and accurately select the best available technique, making it suitable primarily for marine pollution preparedness and response planning in strategic risk assessments.

Conceptualizing the Contextual Dynamics of Safety Climate and Safety Culture Research: A Comparative Scientometric Analysis.

Safety climate and safety culture are important research domains in risk and safety science, and various industry and service sectors show significant interest in, and commitment to, applying its concepts, theories, and methods to enhance organizational safety performance. Despite the large body of literature on these topics, there are disagreements about the scope and focus of these concepts, and there is a lack of systematic understanding of their development patterns and the knowledge domains on which these are built. This article presents a comparative analysis of the literature focusing on safety climate and safety culture, using various scientometric analysis approaches and tools. General development patterns are identified, including the publication trends, in terms of temporal and geographical activity, the science domains in which safety culture and safety climate research occurs, and the scientific domains and articles that have primarily influenced their respective development. It is found that the safety culture and safety climate domains show strong similarities, e.g., in dominant application domains and frequently occurring terms. However, safety culture research attracts comparatively more attention from other scientific domains, and the research domains rely on partially different knowledge bases. In particular, while measurement plays a role in both domains, the results suggest that safety climate research focuses comparatively more on the development and validation of questionnaires and surveys in particular organizational contexts, whereas safety culture research appears to relate these measurements to wider organizational features and management mechanisms. Finally, various directions for future research are identified based on the obtained results.

Forty Years of Risk Analysis: A Scientometric Overview.

Risk Analysis was first published in 1981, established with a vision to provide a platform for inquiry into fundamental risk-related concepts and theories, and to disseminate new knowledge about methods and approaches for identifying, analyzing, evaluating, managing, and communicating risk. The journal has also contributed significantly to a scientific understanding of specific risks related to human health and safety, engineering, ecological, and social systems. Published on behalf of the Society for Risk Analysis, the journal has become a leading platform over its 40-year history. Complementing recent celebratory overviews and perspectives on the evolution, achievements, and future challenges for Risk Analysis, this article presents a scientometric overview of the journal between 1981 and 2020. The study presents high-level insights in the journal publication trends and structure and trends in the leading countries/regions, institutions, and authors, in relation to their respective collaboration networks. Furthermore, the structure and evolution of research focus issues is analyzed, and highly cited publications are identified. The findings are primarily intended to provide high-level insights, which may be useful for early career academics and risk practitioners to understand the structure and development of the research domain, and its main contributors and topics, and for experienced researchers to reflect on the achievements and future developments.

A risk management framework for maritime Pollution Preparedness and Response: Concepts, processes and tools.

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.

Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions.

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.

The Landscape of Risk Communication Research: A Scientometric Analysis.

Risk communication is a significant research domain with practical importance in supporting societal risk governance and informed private decision making. In this article, a high-level analysis of the risk communication research domain is performed using scientometrics methods and visualization tools. Output trends and geographical patterns are identified, and patterns in scientific categories determined. A journal distribution analysis provides insights into dominant journals and the domain's intellectual base. Thematic clusters and temporal evolution of focus topics are obtained using a terms analysis, and a co-citation analysis provides insights into the evolution of research fronts and key documents. The results indicate that the research volume grows exponentially, with by far most contributions originating from Western countries. The domain is highly interdisciplinary, rooted in psychology and social sciences, and branching mainly into medicine and environmental sciences. Narrative themes focus on risk communication in medical and societal risk governance contexts. The domain originated from public health and environmental concerns, with subsequent research fronts addressing risk communication concepts and models. Applied research fronts are associated with environmental hazards, public health, medical risks, nuclear power, and emergency response to various natural hazards. Based on the results, various avenues for future research are described.

The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review.

The COVID-19 global pandemic has generated an abundance of research quickly following the outbreak. Within only a few months, more than a thousand studies on this topic have already appeared in the scientific literature. In this short review, we analyse the bibliometric aspects of these studies on a macro level, as well as those addressing Coronaviruses in general. Furthermore, through a scoping analysis of the literature on COVID-19, we identify the main safety-related dimensions that these studies have thus far addressed. Our findings show that across various research domains, and apart from the medical and clinical aspects such as the safety of vaccines and treatments, issues related to patient transport safety, occupational safety of healthcare professionals, biosafety of laboratories and facilities, social safety, food safety, and particularly mental/psychological health and domestic safety have thus far attracted most attention of the scientific community in relation to the COVID-19 pandemic. Our analysis also uncovers various potentially significant safety problems caused by this global health emergency which currently have attracted only limited scientific focus but may warrant more attention. These include matters such as cyber safety, economic safety, and supply-chain safety. These findings highlight why, from an academic research perspective, a holistic interdisciplinary approach and a collective scientific effort is required to help understand and mitigate the various safety impacts of this crisis whose implications reach far beyond the bio-medical risks. Such holistic safety-scientific understanding of the COVID-19 crisis can furthermore be instrumental to be better prepared for a future pandemic.

Slip and Fall Incidents at Work: A Visual Analytics Analysis of the Research Domain.

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.

A Bayesian Network risk model for assessing oil spill recovery effectiveness in the ice-covered Northern Baltic Sea.

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.

An online platform for rapid oil outflow assessment from grounded tankers for pollution response.

The risk of oil spills is an ongoing societal concern. Whereas several decision support systems exist for predicting the fate and drift of spilled oil, there is a lack of accurate models for assessing the amount of oil spilled and its temporal evolution. In order to close this gap, this paper presents an online platform for the fast assessment of tanker grounding accidents in terms of structural damage and time-dependent amount of spilled cargo oil. The simulation platform consists of the definition of accidental scenarios; the assessment of the grounding damage and the prediction of the time-dependent oil spill size. The performance of this integrated online simulation environment is exemplified through illustrative case studies representing two plausible accidental grounding scenarios in the Gulf of Finland: one resulting in oil spill of about 50 t, while in the other the inner hull remained intact and no spill occurred.

Risk management model of winter navigation operations.

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.

A risk analysis of winter navigation in Finnish sea areas.

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 40cm 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.

A probabilistic model for accidental cargo oil outflow from product tankers in a ship-ship collision.

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.