scholarly journals Collision Risk Assessment Support System for MASS RO and VTSO Support in Multi-Ship Environment of Vessel Traffic Service Area

2021 ◽  
Vol 9 (10) ◽  
pp. 1143
Author(s):  
Yunja Yoo ◽  
Jin-Suk Lee
Keyword(s):  
Risk Assessment ◽  
Risk Analysis ◽  
Real Time ◽  
Support System ◽  
Situational Awareness ◽  
International Association ◽  
Collision Risk ◽  
Surface Ship ◽  
System A ◽  
Marine Accidents

The discussions by the International Maritime Organization for the introduction of a maritime autonomous surface ship (MASS) began in earnest. At the 27th ENAV meeting, the International Association of Marine Aids to Navigation and Lighthouse Authorities proposed the “sharing of a common operating picture for situational awareness of the waterway within vessel traffic service (VTS) environment” when developing a system to support MASS operation. Marine accidents caused by collisions on waterways still account for a high percentage of ship accidents that occur at sea, and many studies have investigated the risk of collision between ships. Collision risk assessment was primarily conducted in ship domain-based safety areas. This study evaluates the collision risk using the ship domain derived by the VTS operator (VTSO) and proposes a real-time collision risk assessment support system to improve the situational awareness of VTSOs and MASS remote operators (MASS ROs) regarding near-collision situations occurring in local waters. To evaluate the validity of the proposed system, a risk analysis was performed on near-collision scenarios at Busan Port. The results show that the distance to the closest point of approach (CPA), time to the CPA, and inter-ship distance converged within 0.5 nautical miles, 10 min, and 3 nautical miles, respectively.

scholarly journals Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

2015 ◽  
Vol 12 (4) ◽  
pp. 1327-1388 ◽  
Author(s):  
R. Fernandes ◽  
F. Braunschweig ◽  
F. Lourenço ◽  
R. Neves
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Oil Spill ◽  
Situational Awareness ◽  
Assessment Tool ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Automatic Identification ◽  
Decision Support Model ◽  
Risk Levels

Abstract. The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable shoreline risk levels from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area – Portuguese Continental shelf – with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time. Shoreline risks can be computed in real-time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical data can help finding typical risk patterns, "hot spots" or developing sensitivity analysis to specific conditions, whereas real time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

scholarly journals Multi-Ship Collision Avoidance Decision-Making Based on Collision Risk Index

2020 ◽  
Vol 8 (9) ◽  
pp. 640
Author(s):  
Yingjun Hu ◽  
Anmin Zhang ◽  
Wuliu Tian ◽  
Jinfen Zhang ◽  
Zebei Hou
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Risk Analysis ◽  
Real Time ◽  
Collision Avoidance ◽  
Evaluation Method ◽  
Risk Index ◽  
Collision Risk ◽  
Ship Collision ◽  
Risk Thresholds

Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

Risk assessment of collisions of an autonomous passenger ferry

2021 ◽  
pp. 1748006X2110507
Author(s):  
Chuanqi Guo ◽  
Stein Haugen ◽  
Ingrid B Utne
Keyword(s):  
Risk Assessment ◽  
Sensitivity Analysis ◽  
Risk Analysis ◽  
Failure Modes ◽  
Bayesian Belief Network ◽  
Collision Risk ◽  
Initial Attempt ◽  
Safety Review ◽  
Autonomous Ship ◽  
Passenger Ferry

Autonomous transportation is an increasingly popular concept and is gradually becoming a reality. This transformation also changes the way people travel. For example, the autonomous ferry is an emerging alternative for residents living in coastal areas. To evaluate the safety of an autonomous ferry, a thorough safety review is necessary. This paper makes an initial attempt by developing a model for performing a risk assessment of collisions between an autonomous ship with manned vessels and applying this to a specific ferry operating in a canal. The safety barriers to prevent a collision are identified, as well as the respective failure modes. A Bayesian belief network is employed to model the collision and to quantitively assess the collision risk of the autonomous ferry. Relevant data are collected to perform a quantitative risk analysis. By running the model, the likelihood of a collision is calculated. A sensitivity analysis is also performed to identify the most contributing causes.

scholarly journals Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

2017 ◽  
Author(s):  
Nicola Paltrinieri ◽  
Gabriele Landucci ◽  
Pierluigi Salvo Rossi
Keyword(s):  
Risk Assessment ◽  
Risk Analysis ◽  
Real Time ◽  
Risk Evaluation ◽  
Oil And Gas ◽  
Time Data ◽  
Dynamic Risk ◽  
System Risk ◽  
Offshore Oil And Gas ◽  
Offshore Oil

Recent major accidents in the offshore oil and gas (O&G) industry have showed inadequate assessment of system risk and demonstrated the need to improve risk analysis. While direct causes often differ, the failure to update risk evaluation on the basis of system changes/modifications has been a recurring problem. Risk is traditionally defined as a measure of the accident likelihood and the magnitude of loss, usually assessed as damage to people, to the environment, and/or economic loss. Recent revisions of such definition include also aspects of uncertainty. However, Quantitative Risk Assessment (QRA) in the offshore O&G industry is based on consolidated procedures and methods, where periodic evaluation and update of risk is not commonly carried out. Several methodologies were recently developed for dynamic risk analysis of the offshore O&G industry. Dynamic fault trees, Markov chain models for the life-cycle analysis, and Weibull failure analysis may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available. However, the mentioned techniques are not widely applied in the common O&G offshore practice due to several reasons, among which their complexity has a primary role. More intuitive approaches focusing on a selected number of critical factors have also been suggested, such as the Risk Barometer or the TEC2O. Such techniques are based on the evaluation of technical, operational and organizational factors. The methodology allows supporting periodic update of QRA by collecting and aggregating a set of indicators. However, their effectiveness relies on continuous monitoring activity and realtime data capturing. For this reason, this contribution focuses on the coupling of such methods with sensors of different nature located in or around and offshore O&G system. The inheritance from the Centre for Integrated Operations in the Petroleum Industries represents the basis of such study. Such approach may be beneficial for several cases in which (quasi) real-time risk evaluation may support critical operations. Two representative cases have been described: i) erosion and corrosion issues due to sand production; and ii) oil production in environmental sensitive areas. In both the cases, dynamic risk analysis may employ real-time data provided by sand, corrosion and leak detectors. A simulation of dynamic risk analysis has demonstrated how the variation of such data can affect the overall risk picture. In fact, this risk assessment approach has not only the capability to continuously iterate and outline improved system risk pictures, but it can also compare its results with sensor-measured data and allow for calibration. This can potentially guarantee progressive improvement of the method reliability for appropriate support to safety-critical decisions.

scholarly journals Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches

2022 ◽  
Vol 10 (1) ◽  
pp. 112
Author(s):  
Konrad Wolsing ◽  
Linus Roepert ◽  
Jan Bauer ◽  
Klaus Wehrle
Keyword(s):  
Risk Assessment ◽  
Anomaly Detection ◽  
Situational Awareness ◽  
Automatic Detection ◽  
Use Cases ◽  
Research Articles ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Potential Use

The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations.

A Decision-support System for Real-time Risk Assessment of Airborne Spread of the Foot-and-Mouth Disease Virus

2005 ◽  
Vol 44 (04) ◽  
pp. 590-595 ◽  
Author(s):  
K. Fuchs ◽  
F. Rubel
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Dispersion Model ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Gaussian Dispersion Model

Summary Objectives: The application of epidemic models during the first days following the confirmation of a virus outbreak should significantly contribute to minimize its costs. Here we describe the first version of a decision-support system for the calculation of the airborne spread of a virus and its application to foot-and-mouth disease (FMD). The goal is to provide geographical maps depicting infection risk for various animal species to support the national health authorities. Methods: The major tool of the decision-support system is a specific epidemic (or atmospheric) model: A so-called Gaussian dispersion model to calculate 3-dimensional virus plumes. Additional tools providing input data and visualizing the output are: A veterinary data base of geo-referenced premises, a geographical information system (GIS), and, as an external part running at the National Weather Service, a numerical weather prediction (NWP) model. To demonstrate the features of the decision-support system a pilot study in Styria, Austria, has been performed simulating an artificial FMD outbreak. Results: One result of this simulation experiment is the determination of neighboring premises at which animals are at risk to be infected. Particular attention has been turned to cattle, sheep and swine. Using actual hourly NWP data from April 25, 2003, and a source of ten swine excreting a virus, cattle have been estimated to be at risk downwind 1,000-12,000 m, sheep 200-1,300 m, and swines 70-330 m. Conclusions: A system for real-time risk assessment of the airborne spread of a virus, applied to FMD, was introduced. Due to the forcing of the Gaussian dispersion model with NWP data, it is designed to run in both analysis and forecast mode. The system was applied for the first time during the Austrian real-time exercise on FMD, instructed by the European Union, in November 2004.

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