Bayesian networks for maritime traffic accident prevention: benefits and challenges

Seaport infrastructure risk assessment for hazardous cargo operations using Bayesian networks

Seaport infrastructure requires considerable resources and time for a full recovery from accidents caused by hazardous cargo. Despite their severity, the risk to seaport infrastructure from hazardous cargo operations has been insufficiently explored. This study aims to fill that gap by examining the risks to seaport infrastructure from the complex effects of hazardous cargo operations. It draws on literature, incident reports, and expert consultations to identify comprehensive risk factors and their interconnections. The study employs expert judgments alongside logistic regression to develop Conditional Probability Tables (CPTs) and conducts a risk analysis using Bayesian networks (BN). Our findings indicate that, under typical operating conditions, fire and explosion, corrosion, and improper handling are the most significant contributors to seaport infrastructure risk with probabilities of 8.73 %, 5.88 %, and 5.61 % respectively. Inverse propagation indicates that the contribution of improper handling and corrosion is enhanced by 153 % and 96 % respectively towards the increased risk. A sensitivity analysis was carried out to pinpoint critical risk factors. Based on these insights, the study suggests practical measures like the use of tracking and monitoring systems along with third-party audits for effective handling, augmented and virtual reality for advanced training, and automation technology for reduced human roles to subside risks to seaport infrastructure and promote uninterrupted operations.

How do navy escorts influence piracy risk in East Africa? A Bayesian network approach

Navy escorts are considered crucial in countering illegal piracy attacks. In this paper, a novel approach is developed to investigate the effect of navy escorts on piracy incidents by models based on two enhanced Tree-Augmented Naïve (TAN) Bayesian networks. This approach offers a systematic investigation into the various factors that influence pirate activities, and helps to identify changes in piracy attack behaviors when confronted by navy escorts and assess the effectiveness of anti-piracy measures. An empirical study is conducted utilizing a unique data set compiled from multiple sources from 2000 to 2019. The empirical evidence shows that there was a gradual reduction in the incidence of piracy attacks in East Africa following the implementation of navy escorts in 2009, but with a surge in 2010 and 2011. The data set is, thus, divided into two time periods at the point of 2009 to facilitate a robust and comprehensive analysis, resulting in the development of two TAN models. Meanwhile, the geographical distribution of pirate attacks has shifted from international waters to port areas and territorial waters. We argue that the surge and geographical shift could be attributed to the calculating behavior of pirates when they encounter external pressures. Finally, a Shapely approach is introduced to evaluate the potential effectiveness of the implemented risk management strategies from a Game Theory perspective. This study offers new insights into the promotion of navy escorts and contributes to the development of a framework for assessing piracy risks in uncertain and dynamic anti-piracy environments.

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship

The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident's probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships

With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships' (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship's configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems' availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.

Scientific awareness of marine accidents in Europe: A bibliometric and correspondence analysis

Marine accidents are a significant issue that can lead to loss of life, damage to ships and cargo, and harm to the marine environment. To gain better understanding of scientific awareness of marine accidents occurred in European countries, this study conducted a bibliometric and correspondence analysis of the scientific literature. Bibliometric analysis was employed to examine various publications, which were released during the period between 2012 and December 2022. Moreover, correspondence analysis was used to classify and analyze marine accidents based on the severity and the consequence of the accidents. The findings indicate that scientific awareness of researchers in countries where serious and very serious marine accidents occur is also high. However, Norway stands out as the country with the highest scientific awareness of researchers despite experiencing marine accidents with less serious. The most significant factor contributing to the prominence of researchers here is their collaborations with researchers from other countries. Overall, this study sheds light on the need for further research and action to improve marine accident prevention. Collaborative efforts involving researchers, maritime stakeholders, and policymakers are necessary to address the complex challenges of marine accidents and to ensure the maritime safety and protection of the marine environment.

A content-aware corpus-based model for analysis of marine accidents

In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions.

Maritime accidents in the Yangtze River: A time series analysis for 2011-2020

The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.

An Integrated Quantitative Risk Assessment Method for Underground Engineering Fires

Fires are one of the main disasters in underground engineering. In order to comprehensively describe and evaluate the risk of underground engineering fires, this study proposes a UEF risk assessment method based on EPB-FBN. Firstly, based on the EPB model, the static and dynamic information of the fire, such as the cause, occurrence, hazard, product, consequence, and emergency rescue, was analyzed. An EPB model of underground engineering fires was established, and the EPB model was transformed into a BN structure through the conversion rules. Secondly, a fuzzy number was used to describe the state of UEF variable nodes, and a fuzzy conditional probability table was established to describe the uncertain logical relationship between UEF nodes. In order to make full use of the expert knowledge and empirical data, the probability was divided into intervals, and a triangulated fuzzy number was used to represent the linguistic variables judged by experts. The α-weighted valuation method was used for de-fuzzification, and the exact conditional probability table parameters were obtained. Through fuzzy Bayesian inference, the key risk factors can be identified, the sensitivity value of key factors can be calculated, and the maximum risk chain can be found in the case of known evidence. Finally, the method was applied to the deductive analysis of three scenarios. The results show that the model can provide realistic analysis ideas for fire safety evaluation and emergency management of underground engineering. The proposed EPB risk assessment model provides a new perspective for the analysis of UEF accidents and contributes to the ongoing development of UEF research.

Spatial Modeling of Maritime Risk Using Machine Learning

Managing navigational safety is a key responsibility of coastal states. Predicting and measuring these risks has a high complexity due to their infrequent occurrence, multitude of causes, and large study areas. As a result, maritime risk models are generally limited in scale to small regions, generalized across diverse environments, or rely on the use of expert judgement. Therefore, such an approach has limited scalability and may incorrectly characterize the risk. Within this article a novel method for undertaking spatial modeling of maritime risk is proposed through machine learning. This enables navigational safety to be characterized while leveraging the significant volumes of relevant data available. The method comprises two key components: aggregation of historical accident data, vessel traffic, and other exploratory features into a spatial grid; and the implementation of several classification algorithms that predicts annual accident occurrence for various vessel types. This approach is applied to characterize the risk of collisions and groundings in the United Kingdom. The results vary between hazard types and vessel types but show remarkable capability at characterizing maritime risk, with accuracies and area under curve scores in excess of 90% in most implementations. Furthermore, the ensemble tree-based algorithms of XGBoost and Random Forest consistently outperformed other machine learning algorithms that were tested. The resultant potential risk maps provide decisionmakers with actionable intelligence in order to target risk mitigation measures in regions with the greatest requirement.

Accident Prevention Analysis: Exploring the Intellectual Structure of a Research Field

Accident prevention is of great significance in avoiding or reducing all kinds of casualties and economic losses, and is one of the main challenges for social sustainable development. Hence, it has been an active research field for many decades around the world. To master the research status of accident prevention, and explore the knowledge base and hot trends, 1294 papers from the WOS retrieval platform SCIE and SSCI databases from 1990 to 2021 were selected as data samples. Co-occurrence analysis, co-citation analysis, co-authorship analysis, and keyword analysis were performed on the literature on accident prevention research with bibliometric analysis methods. The study showed that the United States ranked first in the number of publications of any country/region and Georgia Inst Technol ranked first in the number of institutional publications. System analysis and accident model establishment, analysis of construction accidents, road accident prevention, and safety culture and safety climate are the knowledge base in the accident prevention studies and the core journals in this field are Safety Science, Accident Analysis and Prevention, Pediatrics, and Reliability Engineering & System Safety. There are four major research hotspots in accident prevention studies: routine accident prevention, model-based research, systems analysis and accident prediction, and occupational safety and public health research. At present, the basic theory and structural system of accident prevention research have been basically established, with many research directions and a wide range of frontier branches. Safety management, public safety, Bayesian networks, and simulation are the research frontiers of accident prevention.

Development and testing of a tool to measure the organizational safety climate aboard US Navy ships

INTRODUCTION

Safety climate is a critical human factor that can increase safety-related behaviors and reduce accidents. This research reports on a three-phase program of development and validation of a safety climate survey tool initiated by U.S. Naval Surface Forces after numerous accidents and near misses.

METHOD

The initial survey was administered to 4,042 sailors aboard 30 warships, and factor analysis supported a three-factor measure of a safety climate composed of operational compliance, positive work environment, and organizational resources. The predictive validity of the newly developed safety climate measure was tested against the number of accidents reported in the 12 months after the safety climate survey.

RESULTS

This analysis revealed that a positive work environment and operational compliance were linked to fewer accidents; surprisingly, organizational resources were linked to more accidents. Implications for future research on safety climate and occupational safety are discussed.

Prediction of Run-Off Road Crash Severity in South Korea’s Highway through Tree Augmented Naïve Bayes Learning

The run-off road crash (RORC) is a representative type of lethal crash. The severity of RORC has increased owing to a combination of factors, such as roadside geometry, traffic conditions, and weather/climatic conditions. In this study, a model for estimating the RORC severity was developed based on various factors, including fixed objects, roadway geometry, traffic conditions, and road traffic environment. To develop the model, the accident data of crashes with roadside fixed objects on highways, as well as information on fixed object-related variables and roadway geometry-related variables, were collected. To improve the model in terms of implementing a close reflection of the real world, a learning method with tree augmented naïve Bayes (TAN), which takes into account the causal links between variables, was applied. The results of the analysis showed that the severity of crashes with roadside fixed objects increased sharply when the vertical slope was ≥4%, the radius of the curve was ≥250 m, the distance between the fixed object and the roadway was less than 3 m, or the density of fixed objects installation was greater than 2 for every 10 m. The proposed model allows for an analysis of sections with a high RORC severity on the roadways in operation and provides improvement measures to reduce the severity of RORC.

The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment

An integrative approach to maritime accident risk factor assessment in accordance with formal safety assessment is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation of modelling, verification, and validation. The methodology for probabilistic modelling with BNs is well known and its application to risk assessment is based on the model verified though sensitivity analysis only, while validation of the model is often omitted due to a lack of established evaluation measures applicable to scarce real-world data. For this reason, in this work, the modified Lyapunov divergence measure is proposed as a novel quantitative assessor that can be efficiently exploited on an individual accident scenario for contributing causal factor identification, and thus can serve as the measure for validation of the developed expert elicited BN. The proposed framework and its approach are showcased for maritime grounding of small passenger ships in the Adriatic, with the complete grounding model disclosed, quantitative validation performed, and its utilization for causal factor identification and risk factor ranking presented. The data from two real-world grounding cases demonstrate the explanatory capabilities of the developed approach.

Quantitative Analysis on Risk Influencing Factors in the Jiangsu Segment of the Yangtze River

Quantitative risk influencing factors (RIFs) are proposed, using the Conjugate Bayesian update approach to analyze 945 collision accidents and incidents cases from the Jiangsu Segment of the Yangtze River over five years from 2012 to 2016. The accident probability is compared under a pairwise comparison mode in order to reflect the relative risk between accidental situations. The Bayesian update mode is constructed to quantitatively evaluate the relative importance of different RIFs. The riskiest segment of Jiangsu Waterways as well the main causations of collisions are identified based on the distributions of collision risk in the six segments of the waterways. The results can support managers to develop the most effective policies to mitigate the collision risk.

Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

INTRODUCTION

Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity.

METHOD

In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant variables.

RESULTS

In terms of balanced accuracy and AUCs, the proposed approach performed reasonably well. Bayesian modeling results indicated that the prior crash/violation experiences of road users and roadways were very important risk indicators. For example, migrant workers tend to have high injury risk due to their dangerous violation behaviors, such as retrograding, red-light running, and right-of-way violation. Furthermore, results showed that certain variable combinations had enhanced impacts on severity outcome than single variables. For example, when a migrant worker and a non-motorized vehicle are involved in a crash happening on a local road with high cumulative violation frequency in the previous year, the probability for drivers suffering serious injury or fatality is much higher than that caused by any single factor. Practical applications: The proposed methodology and modeling results provide insights for developing effective countermeasures to reduce crash severity and improve traffic system safety performance.

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.

A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector

In production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Markov method, this paper proposed a fuzzy Markov method that takes the full advantages of the three methods and makes the analysis of risk, reliability, availability and uncertainty all in one. This method uses the fault tree and fuzzy theory to preprocess the input failure data to improve the reliability of the input failure data, and then input the preprocessed failure data into the Markov model; after that iterate and adjust the model when uncertainty events occur, until the data of all events have been processed by the model and the updated model obtained, which best reflects the system state. The wellhead connector of a subsea production system was used as a case study to demonstrate the above method. The obtained reliability index (mean time to failure) of the connector is basically consistent with the failure statistical data from the offshore and onshore reliability database, which verified the accuracy of the proposed method.

Risk Assessment Methodology for Vessel Traffic in Ports by Defining the Nautical Port Risk Index

Ports represent a key element in the maritime transportation chain. Larger vessels and higher traffic volumes in ports might result in higher risks at the navigational level. Thus, the dire need for a comprehensive and efficient risk assessment method for ports is felt. Many methodologies have been proposed so far, but their application to aggregated vessel traffic risks for the overall assessment of ports is not developed yet. Hence, the development of an approach for the appraisal of the vessel traffic risks is still a challenging issue. This research aims to develop an assessment methodology to appraise the potential risk of accident occurrence in port areas at an aggregated level by creating a ‘Nautical Port Risk Index’ (NPRI). After identifying the main nautical risks in ports, the Analytic Network Process (ANP) is used to derive the risk perception (RP) weights for each criterion from data collected through surveys to expert navigators. The consequences related to each nautical risk are identified in consultation with risk experts. By combining the RP values and the consequence of each criterion for a time period, the NPRI is calculated. The risks in the Port of Rotterdam are presented in a case study, and the method has been validated by checking the results with experts in assessing nautical port risks from the Port of Rotterdam Authority. This method can be used to assess any new port design, the performance of different vessel traffic management measures, changes in the fleet composition, or existent ports using the Automatic Identification System (AIS) data.

Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics

Accidents occur frequently in traffic-intensive waters, which restrict the safe and rapid development of the shipping industry. Due to the suddenness, randomness, and uncertainty of accidents in traffic-intensive waters, the probability of the risk factors causing traffic accidents is usually high. Thus, properly analyzing those key risk factors is of great significance to improve the safety of shipping. Based on the analysis of influencing factors of ship navigational risks in traffic-intensive waters, this paper proposes a cloud model to excavate the factors affecting navigational risk, which could accurately screen out the key risk factors. Furthermore, the risk causal model of ship navigation in traffic-intensive waters is constructed by using the infectious disease dynamics method in order to model the key risk causal transmission process. Moreover, an empirical study of the Yangtze River estuary is conducted to illustrate the feasibility of the proposed models. The research results show that the cloud model is useful in screening the key risk factors, and the constructed causal model of ship navigational risks in traffic-intensive waters is able to provide accurate analysis of the transmission process of key risk factors, which can be used to reduce the navigational risk of ships in traffic-intensive waters. This research provides both theoretical basis and practical reference for regulators in the risk management and control of ships in traffic-intensive waters.

Identifying characteristics that impact motor carrier safety using Bayesian networks

PROBLEM STATEMENT

In the U.S., a safety rating is assigned to each motor carrier based on data obtained from the Motor Carrier Management Information System (MCMIS) and an on-site investigation. While researchers have identified variables associated with the safety ratings, the specific direction of the relationships are not necessarily clear.

OBJECTIVE

The objective of this study is to identify those relationships involved in the safety ratings of interstate motor carriers, the largest users of the U.S. transportation network.

METHOD

Bayesian networks are used to learn these relationships from data obtained from MCMIS for a 6-year period (2007-2012).

RESULTS

Our study shows that safety rating assignment is a complex process with only a subset of the variables having statistically significant relationship with safety rating. They include driver out-of-service violations, weight violations, traffic violations, fleet size, total employed drivers, and passenger & general carrier indicators.

APPLICATION

The findings have both immediate implications and long term benefits. The immediate implications relate to better identification of unsafe motor carriers, and the long term benefits pertain to policies and crash countermeasures that can enhance carrier safety.

Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China

As a critical configuration of interchanges, the weaving section is inclined to be involved in more traffic accidents, which may bring about severe casualties. To identify the factors associated with traffic accidents at the weaving section, we employed the multinomial logistic regression approach to identify the correlation between six categories of risk factors (drivers' attributes, weather conditions, traffic characteristics, driving behavior, vehicle types and temporal-spatial distribution) and four types of traffic accidents (rear-end, side wipe, collision with fixtures and rollover) based on 768 accident samples of an observed weaving section from 2016 to 2018. The modeling results show that drivers' gender and age, weather condition, traffic density, weaving ratio, vehicle speed, lane change behavior, private cars, season, time period, day of week and accident location are important factors affecting traffic accidents at the weaving section, but they have different contributions to the four traffic accident types. The results also show that traffic density of ≥31 vehicle/100 m has the highest risk of causing rear-end accidents, weaving ration of ≥41% has the highest possibility to bring about a side wipe incident, collision with fixtures is the most likely to happen in snowy weather, and rollover is the most likely incident to occur in rainy weather.

Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

Many causal factors to marine traffic accidents (MTAs) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to accident mechanisms, the complex structural chains on causes to MTA systems were analyzed by combining the human failure analysis and classification system (HFACS) with theoretical structural equation modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of a MTA, and the constituent elements of the causes of the accident were conducted. Second, a hypothetical model of human factors classification was proposed by applying the practice of the structural model. Third, with the data resources from ship accident cases, this hypothetical model was discussed and simulated, and as a result, the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behavior. Application examples show that relationships in the HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

Advanced uncertainty modelling for container port risk analysis

Globalization has led to a rapid increase of container movements in seaports. Risks in seaports need to be appropriately addressed to ensure economic wealth, operational efficiency, and personnel safety. As a result, the safety performance of a Container Terminal Operational System (CTOS) plays a growing role in improving the efficiency of international trade. This paper proposes a novel method to facilitate the application of Failure Mode and Effects Analysis (FMEA) in assessing the safety performance of CTOS. The new approach is developed through incorporating a Fuzzy Rule-Based Bayesian Network (FRBN) with Evidential Reasoning (ER) in a complementary manner. The former provides a realistic and flexible method to describe input failure information for risk estimates of individual hazardous events (HEs) at the bottom level of a risk analysis hierarchy. The latter is used to aggregate HEs safety estimates collectively, allowing dynamic risk-based decision support in CTOS from a systematic perspective. The novel feature of the proposed method, compared to those in traditional port risk analysis lies in a dynamic model capable of dealing with continually changing operational conditions in ports. More importantly, a new sensitivity analysis method is developed and carried out to rank the HEs by taking into account their specific risk estimations (locally) and their Risk Influence (RI) to a port's safety system (globally). Due to its generality, the new approach can be tailored for a wide range of applications in different safety and reliability engineering and management systems, particularly when real time risk ranking is required to measure, predict, and improve the associated system safety performance.

A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work

Accidents in Repair, Maintenance, Alteration, and Addition (RMAA) work have become a growing concern, in recent years. The repair and maintenance works of electrical and mechanical (E&M) installations involves a variety of trades, a large number of practitioners and a series of high-risk activities. The uniqueness of E&M work, in the RMAA sector, requires a discrete and specific research to improve its safety performance. Understanding the causal relationships between safety factors and the number of accidents becomes crucial to develop a more effective safety management strategy. The Bayesian Network (BN) model is proposed to establish a probabilistic relational network between the causal factors, including both safety climate factors and personal experience factors that have influences on the number of accidents related to E&M RMAA work. The data were collected using a survey questionnaire, involving a hundred and fifty-five E&M practitioners. The BN results demonstrated that safety attitude and safety procedures were the most important factors to reduce the number of accidents. The proposed BN provides the ability to find out the most effective strategy with the best utilization of resources, to reduce the chance of a high number of E&M accidents, by controlling a single factor or simultaneously controlling, both, the safety climate and personal factors, to improve safety performance.

Understanding the Outcome in the Chinese Changjiang Disaster in 2015: A Retrospective Study

BACKGROUND

Rescue after a maritime disaster remains a great challenge in emergency medicine.

OBJECTIVE

We performed an overview of rescue efforts among the victims in the sunken cruise ship Eastern Star in the 2015 Changjiang River marine disaster, as well as possible preventive measures in maritime transport situations.

METHODS

The rescue records of 454 victims of the sunken ship were analyzed retrospectively. Their demographic data, rescue effects, accident inducement, and injury disposition were reviewed. A thorough analysis from the point of view of maritime traffic safety was also performed.

RESULTS

Of the 454 victims, 442 (97.36%) were killed and only 12 (2.64%) survived. The survivors were classified based on their gender, rescue type, and rescue spot as follows: male (91.67%), female (8.33%); tourists (50.00%), and ship staff (50.00%), after the breakdown of the rescue spot in Jianli, Hubei province, China. The survivors were saved only during the initial 17 h after the disaster. The survivors suffering from somato- and psychotrauma were urgently treated for limb injuries, infections of the upper respiratory tract and lungs, fluid and electrolyte imbalance, and acute traumatic stress. This incident was the most severe maritime disaster since the establishment of the People's Republic of China on October 1, 1949, due to the large number of elderly victims, fast overturning speed, and severe weather.

CONCLUSIONS

Emergency rescue requires more automated and intelligent systems for maritime safety. An increased focus must be placed on public welfare and ethics, with the goal of influencing more prosocial behavior rather than the pursuit of profit.

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.

Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks

This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port.

A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: A Bayesian network model for the Gulf of Finland

Large-scale oil accidents can inflict substantial costs to the society, as they typically result in expensive oil combating and waste treatment operations and have negative impacts on recreational and environmental values. Cost-benefit analysis (CBA) offers a way to assess the economic efficiency of management measures capable of mitigating the adverse effects. However, the irregular occurrence of spills combined with uncertainties related to the possible effects makes the analysis a challenging task. We develop a probabilistic modeling approach for a CBA of oil spill management and apply it in the Gulf of Finland, the Baltic Sea. The model has a causal structure, and it covers a large number of factors relevant to the realistic description of oil spills, as well as the costs of oil combating operations at open sea, shoreline clean-up, and waste treatment activities. Further, to describe the effects on environmental benefits, we use data from a contingent valuation survey. The results encourage seeking for cost-effective preventive measures, and emphasize the importance of the inclusion of the costs related to waste treatment and environmental values in the analysis. Although the model is developed for a specific area, the methodology is applicable also to other areas facing the risk of oil spills as well as to other fields that need to cope with the challenging combination of low probabilities, high losses and major uncertainties.

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.