Ship Collision Risk Research Articles

An improved time discretized non-linear velocity obstacle method for multi-ship encounter detection

Ship collision is one of the major contributors of maritime accidents. Quantitative risk analysis of such accidents is an effective tool for maritime safety administrations to understand the current risk level and propose risk mitigation measures. In this paper, an improved Time Discretized Non-linear Velocity obstacle (TD-NLVO) algorithm is proposed to detect multiple ship encounter situations using historical AIS data. Boolean operation on the individual NLVO is integrated with TD-NLVO using the union of the velocity-obstacle sets to determine a dangerous encounter situation according to the pre-set criteria. Two case studies are implemented to illustrate the capability of the proposed algorithm. A comparison is conducted between the previous and the improved methods. The results indicate that the improved method can effectively identify a multiple ship encounter which satisfies the pre-set criteria. The improved method has the potential to provide more detailed information for stakeholders e.g. maritime safety administration, etc. to propose risk mitigation measures as well as to improve the accuracy of geometric probability analysis for ship collision risk.

Potential risk ship domain as a danger criterion for real-time ship collision risk evaluation

One of the most useful method to indicate the risk of ship collision is the ship domain. After decades of development, different ship domain models have been constructed from various aspects. Nevertheless, a shortcoming of the early research of ship domains is not able to describe the level of danger of any point in the area around the ship. Therefore, a Potential Risk Ship Domain (PRSD) model with clear meaning of risk degree is first proposed in this paper. The zone around a ship has been established which is based on concept of ship domain and consists of continuously risk levels, by using kernel-density algorithm. The influence of ship length and speed as well as navigation situation on the size and shape of ship domain by PRSD model are analyzed. Size of ship domain and potential collision risk indexes for each risk level will be identified by comparison with previous models. Simulations show that the PRSD model can effectively evaluate the danger of navigation and discovery potentially dangerous areas for navigation by indicating the dynamic and continuous index of potential risk degree in real time.

Evaluation of ship collision risk assessments using environmental stress and collision risk models

In Korea, ship collisions represent the majority of marine accidents and are substantially more likely in port areas where ship traffic is dense. To improve the safe navigation of vessels, the Korea Maritime Safety Audit (KMSA) has been conducting maritime traffic flow evaluations based on the Environmental Stress (ES) model since 2009. The ES model quantifies the subjective stress felt by the navigator; however, it does not reflect the relative bearing between ships. Risk assessment models using the ship domain are designed to consider the conditions of a potential encounter with a target ship and the possibility of avoidance action. However, even if the target ship does not invade the domain of the own ship, collision may occur at the closest point of approach (CPA). Therefore, in this study, the Collision RIsk (CoRI) model which moves the existing ship domain to the CPA is applied to compare the collision risk according to the navigator's awareness of the vessel encounter conditions. The results demonstrate that the CoRI model is a better risk assessment method than the ES model because it sensitively reflects the risks due to collision avoidance action, such as course alteration and speed changes.

Zero-Inflated Exponential Distribution of Casualty Rate in Ship Collision

There are two weaknesses in current researches into human casualty of ship collision. One is that the range of injuries or fatalities is restricted to the maximum number of casualties in a particular sample, which may not cover all the possible numbers of casualties in the future. International Maritime Organization (IMO) employed the injured or dead percentage of all the persons on board to represent casualties, but it only provided several discrete values to quantify human losses in different scenarios. The other is that the assumption that the distributions of the injuries or fatalities follow certain distribution, such as negative binomial and Poisson distributions is left to be statistically tested. Firstly, this study considers casualty rate, including injury and fatality rates, as random variables; the interval of the variables are from 0 to 1. Then, the distributions of the variables are investigated using historical data. From historical data, we can find that there are many zeros. Zero-inflated models are proved to be effective in processing data with inflated zeros. Furthermore, the probability density of the variables decreases rapidly as the casualty rate becomes larger. Thus, zero-inflated exponential distribution is assumed to fit the data. The parameters of zero-inflated exponential distribution are calibrated by maximum likelihood estimation (MLE) method. Finally, the assumption is tested by chi-square test. The zero-inflated exponential distribution can be used to generate human losses as a part of consequences in the simulation of ship collision risk.

Probabilistic ship domain with applications to ship collision risk assessment

Ship domain plays a critical role in ship traffic risk assessment and safe navigation. Today, the widely available AIS data makes it possible to capture the diverse behaviors of navigators and navigational conditions by vessel trajectories in practice. We propose the novel concept of probabilistic ship domain, which depicts the ship domain boundary as the vague value rather than the crisp value with a forbidden boundary and a desired boundary. We also develop an effective big AIS data driven approach to determine the probabilistic ship domain. We further investigate how to use the probabilistic ship domain to assess ship collision risk. Finally, a few case studies from the Singapore Strait are conducted to show the merits of the probabilistic ship domain and the big AIS data driven approach.

Measuring Ship Collision Risk in a Dense Traffic Environment

Collision risk measurement is an essential topic for ship collision prevention. Many risk measures, i.e. DCPA/TCPA, etc., decouple the ship traffic into several pairs of ships and then evaluate the risk in each pair. This kind of measurement loses some information of the entire traffic and might include some biases in risk measurement, especially in multiple‐ship scenarios. In this article, Imminent Collision Risk Assessment (ICRA) is extended, which formulates collision risk as a ratio of reachable maneuvers leading to a collision and all reachable maneuvers (velocities). Two groups of scenarios have been simulated to show the ICRA is suitable for assessing the collision risk in multiple‐ship scenarios. Moreover, two improvements have been introduced: (1) a generalized velocity obstacle algorithm is introduced to collect the maneuvers leading to collisions, which considers ship dynamics; (2) the constraints of forces are considered in the formulation of reachable maneuvers. As a result, the proposed measurement helps one ship assess the risk of approaching obstacles which are difficult to avoid the collision in terms of own‐ship’s dynamics and kinetic constraints.

Prediction of ship collision risk based on CART

The primary function of a collision risk index is to determine the time when ships take action to avoid a collision. In this study, based on the complex non-linear relationship between the collision risk degree and its influencing factors, classification and regression trees (CARTs) are applied to construct a prediction model for ship collision risk. The fuzzy comprehensive evaluation method is used to evaluate the risk of ship encounter samples to build a collision risk identification library containing expert collision avoidance experience information. The authors' proposed CART regression model is trained using the samples in this identification library to develop a collision risk prediction model based on the CART. Their experimental results show that their proposed CART prediction model is better that the existing ship collision risk prediction model in terms of prediction accuracy and prediction speed when the feature dimension is low and the sample size is small.

퍼지로직을 이용한 기상악화 취약성 기반 선박충돌위험도 생성 알고리즘 구축

There have been many studies to analyze the ship collision risk of navigation. However, most papers mainly solve the navigation collision risk using DCPA (Distance to Closest Point Approach) and TCPA (Time to Closest Point Approach). There are limitations for being lack of considering the traffic weather conditions around the ships. In this paper, we propose a novel algorithm to solve the ship’s collision risk based on vulnerability, where DCPA, TCPA and bad weather environment are combined using fuzzy logic for e-Navigation. and its validity is shown with some experiments.

Predictive Evaluation of Ship Collision Risk Using the Concept of Probability Flow

This paper presents a semianalytical approach for evaluating the collision risk between two moving surface ships. The concept of probability flow is introduced to develop an analytically sound problem formulation, which allows for an accurate estimation of collision probability considering time-varying ship trajectory uncertainties. For efficient computation, the flow of collision probability is separated into diffusion and drift components. These two probability components are combined to obtain the instantaneous collision probability, and this instantaneous probability is integrated in time to quantify the expected risk of collision. To demonstrate the feasibility of the proposed approach, traffic simulations are performed for several representative maritime traffic scenarios and the obtained simulation results are discussed.

Distance Identification for Maximum Change in Ship Collision Risk through a Coast Guard Patrol Ship Experiment

Distance Identification for Maximum Change in Ship Collision Risk through a Coast Guard Patrol Ship Experiment

Quantitative analysis of COLREG rules and seamanship for autonomous collision avoidance at open sea

Ship collision avoidance is highly dependent upon seamanship and rules. When ship collision risk exists, proper collision avoidance actions must be taken according to the correct encounter situation and determined stage. All autonomous collision avoidance (ACA) operations in the future must comply with given rules and seamanship practices, which make the quantitative analysis of them prerequisites for ACA. This study presents a novel quantitative analysis system for the International Regulations for Preventing Collisions at Sea (COLREG) Rules and Seamanship. The proposed system consists of three parts: an encounter situation discrimination model based on the mutually relative bearing of the “target ship” (TS) and “own ship” (OS); a stage discrimination model representing the extent of collision risk per different domain models for every potential situation and a model to determine collision avoidance action per COLREG, seamanship, and ship maneuverability information was established accordingly. The collision avoidance plans appropriate for different situations and stages are generated based on the rules, seamanship, and rudder steering direction judgments. A simulation scenario was utilized to validate the effectiveness and feasibility of the system.

A Systematic Approach for Collision Risk Analysis based on AIS Data

Ship collision risk is an important aspect of ship navigation safety. A systematic method to assess collision risk by monitoring parameter states continually is necessary and has proven effective. Another important factor in risk assessment is ship size, but the effect of the size of ship pairs has not been considered properly in many previous studies. This research utilises a systematic perspective to study collision risk of near-misses in ship-ship encounters. This fills a secondary research gap where previous risk assessments only investigated near-misses from the perspective of a single vessel. Following this proposed approach, ship pair encounter states can be continually tracked. Ultimately, a method of improved Vessel Collision Risk Operator (VCRO) to merge risk assessments of both ships is proposed through integration of near-miss collision risks in a systematic way, which overcomes the disadvantages of prior VCROs that only consider the maximum value, from which it is difficult to track and judge the risk trend. Utilising a case study, the effectiveness of the proposed method is validated through analysis of ship encounters, with ships of different sizes in the Baltic Sea.

뉴로-퍼지 기반의 선박 충돌 회피 조치 분석 시스템 설계

선박 충돌 사고를 예방하기 위해 충돌위험도를 미리 추정하여 알려주는 다양한 연구들이 꾸준히 소개되고 있고, 일부 항해장비에 적용되고 있다. 하지만, 충돌위험을 미리 알려주었을 때, 실제 운항자는 충분히 피항 가능한 상황인데 위험성을 알린다고 판단하여 위험 경고를 무시하거나 장비의 알람 기능을 꺼놓는 경우도 많은 것으로 알려져 있다. 선박 충돌 위험 예측이 운항자들에게 좀 더 유용해지기 위해서는 실제 선박들의 습관화된 충돌 회피 동작을 고려할 필요가 있다. 이 연구는 선박에서의 충돌 회피 조치가 어떻게 이루어지고 있는지 조우 유형별로 분석하고 이력을 관리하기 위한 시스템을 제안한다. 특히, 충돌회피를 위한 초기대응에 대한 적절성을 판단하는 핵심 모듈을 뉴로-퍼지 기반 추론 형태로 상세히 설계하고 테스트함으로써 제안하는 시스템의 타당성을 보인다. Various studies on the method of ship collision risk assessment for alarm have been reported constantly, and the result of the studies is applied to navigation devices. However, it is known that navigators ignore or turn off frequent alarms from the devices of predicting collision risk, because they may avoid collisions in the most of situations. In oder to make the prediction of ship collision risk more useful, it is necessary to consider the customary actions of ship collision avoidance. This paper proposes a system of analyzing collision avoidance measures of ships according to the types of encounter and managing the avoidance history of each ship. The core module of the system is designed as a neuro-fuzzy based inference system, and the test of the module validates the proposed system.

Analysis of ship collision damage by combining Monte Carlo simulation and the artificial neural network approach

ABSTRACTShip collision is a highly hazardous accident with potentially serious consequences. Thus, evaluating the ship collision risk with quantitative analysis of damage, which is characterised by uncertainties, is necessary. Monte Carlo simulation (MCS) is a flexible and efficient method for this purpose. However, large computing resources are required for MCS-based risk assessment, especially when numerous random variables are involved. In addition, ship collision damage models are complicated and are usually resolved by the finite element method (FEM), which is computationally expensive. To address this problem, a coupled method is proposed in this paper: the artificial neural network (ANN) model was combined with MCS to assess collision damage quantitatively. A dynamic simulation in the time domain was used to predict the structural response resulting from a ship collision. Then the ANN was trained based on the collision data and used as a substitute for the iterative FEM runs within the MCS procedure. A benchmark collision case is used to verify the practicability of the proposed method, and a qualitative uncertainty analysis is also conducted in this quantitative damage assessment.

Analysis of Collision Risk Perceived by Ship Operators in Ship Collision Risk Situation

Analysis of Collision Risk Perceived by Ship Operators in Ship Collision Risk Situation

퍼지와 DEVS를 이용한 선박 충돌 위험 예측 모델 설계

선박에 현대화된 다양한 항해장비들이 설치됨에도 불구하고 여전히 해양사고가 자주 일어나는데, 이런 사고의 주요 형태 중 하나가 충돌 사고이다. 우리나라 해양사고의 약 1/4이 충돌에 의한 사고이고, 이 중 대부분이 인적오류가 원인인 것으로 알려져 있다. 따라서, 항해사의 의사결정을 도울 수 있는 지능적인 지원 도구가 필요한데, 이와 관련하여 충돌위험을 추정하는 다양한 방식들이 꾸준히 소개되어 왔으며 충돌위험 상황에 대해 사람에게 친숙한 언어적 표현을 반영하여 추론하기 위해 퍼지를 활용한 연구 결과들이 많다. 이런 기존 연구들의 충돌위험도는 현재시점에서 선박들의 속도나 방향 상태가 유지되는 것을 기준으로 충돌위험도를 추정한다. 그러나, 실제 선박에서는 충분히 피항 가능 상황임에도 불구하고 충돌 위험으로 판단되어 잦은 경고를 울리는 시스템들에 대해 항해사들이 느끼는 불편함이 적지 않아 보조 장치들의 알람 기능을 꺼놓은 경우도 많은 것으로 알려져 있다. 이 연구는 선박들의 일반적인 피항 패턴을 반영한 가까운 미래 시점의 충돌위험도 예측에 관한 것으로서, 퍼지추론과 DEVS 형식론에 기반한 충돌 위험 예측 모델을 제안한다. Even thought modernized marine navigation devices help navigators, marine accidents has been often occurred and ship collision is one of the main types of the accidents. Various studies on the assessment method of collision risk have been reported, and studies using fuzzy theory are remarkable for the reason that reflect linguistic and ambiguous criteria for real situations. In these studies, collision risks were assessed on the assumption that the current state of navigation ship would be maintained. However, navigators ignore or turn off frequent alarms caused by the devices predicting collision risk, because they think that they can avoid the collisions in the most of situations. This paper proposes a model of predicting ship collision risk considering the general patterns of collision avoidance, and the approach is based on fuzzy inference and discrete event system specification (DEVS) formalism.

An analysis of domain-based ship collision risk parameters

According to a lot of contemporary research on ship collision avoidance the classic approach parameters – distance at closest point of approach (DCPA) and time to the closest point of approach (TCPA) – are not sufficient for estimating ship collision risk and for planning evasive manoeuvres. Consequently new measures are introduced, often utilizing the concept of a ship domain. Their drawback, up to this point, was the lack of analytic solutions that would make it possible to efficiently use ship domains in real-time systems where computational time is of essence. The current paper aims to change this, offering analytic formulas for domain-based collision risk parameters: degree of domain violation (DDV) and time to domain violation (TDV). Explicit derivations of formulas for DDV and TDV are presented here for any elliptic domain. For domains of other shapes elliptic approximations are discussed, so that the derived formulas could still be used. A comparison of TCPA/DCPA with domain-based parameters is presented, evidencing the superiority of the latter.

Incorporation of human factors into ship collision risk models focusing on human centred design aspects

This paper presents an approach that more adequately incorporates human factor considerations into quantitative risk analysis of ship operation. The focus is on the collision accident category, which is one of the main risk contributors in ship operation. The approach is based on the development of a Bayesian Network (BN) model that integrates elements from the Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) and focuses on the calculation of the collision accident probability due to human error. The model takes into account the human performance in normal, abnormal and critical operational conditions and implements specific tasks derived from the analysis of the task errors leading to the collision accident category. A sensitivity analysis is performed to identify the most important contributors to human performance and ship collision. Finally, the model developed is applied to assess the collision risk of a feeder operating in Dover strait using the collision probability estimated by the developed BN model and an Event tree model for calculation of human, economic and environmental risks.

Reliability-based structural design framework against accidental loads – ship collision

As civil infrastructures become highly complex and interconnected, accidental events such as fire, explosion, dropped objects and collision tend to occur more frequently, and are causing critical structural damage and socio-economic costs. Therefore, accidental loads should be considered appropriately during the structural design. To achieve this goal, the paper presents a reliability-based structural design framework against accidental loads. The proposed framework employs a scenario-based simulation in which the occurrence of an accidental load event and the resulting structural loads are modelled probabilistically. The probabilistic distribution of accidental loads is then determined based on the occurrence probabilities of identified scenarios and the conditional probability of structural loads given each accident scenario. In order to estimate the structural responses under accidental loads, an appropriate type of structural analysis is carried out for the given limit states of the structural resistance at the critical sections. The proposed framework is illustrated by an example of a cable-stayed bridge whose piers are under ship collision risk. To consider the dynamic amplification of bridge response caused by ship–bridge collision, time history analysis is performed for the equivalent static ship collision loads. The paper also identifies research needs and challenges in applying the proposed framework to other accidental loads.

Application of Grey Relational Decision-Making on Determination of Ship Collision Risk Degree

Application of Grey Relational Decision-Making on Determination of Ship Collision Risk Degree