Pentti Kujala

Analysis of the marine traffic safety in the Gulf of Finland

The Gulf of Finland (GOF) is geographically situated between Finland and Estonian waters. The seafloor varies between deep and shallow and a number of underwater rocks exist in the Finnish archipelago area. The marine traffic has been growing fast during the last years in this area, especially due to the rapid increase of the transportation of various cargoes to Russia and the transport of oil from Russia. In this paper the safety of the marine traffic in the GOF area is analysed. First a detail accident statistics during the last 10 years are described and thereafter the risk of ship collisions is studied by theoretical modelling in two locations. Finally the results of the theoretical models are compared with actual accident statistics. The results reveal that grounding is the dominating accident type in these waters and typically about 11 groundings take place annually, of which about one is a tanker grounding. For collision the highest risks are caused by the passenger ship/RoPax ships traffic between Helsinki and Tallinn together with the high traffic intensity eastwards/westward to and from Russian harbours. The theoretical collision models give good results when compared with the accident statistics. AIS data is utilised in the theoretical models to calculate the geometric collision probabilities.

Traffic simulation based ship collision probability modeling

Maritime traffic poses various risks in terms of human, environmental and economic loss. In a risk analysis of ship collisions, it is important to get a reasonable estimate for the probability of such accidents and the consequences they lead to. In this paper, a method is proposed to assess the probability of vessels colliding with each other. The method is capable of determining the expected number of accidents, the locations where and the time when they are most likely to occur, while providing input for models concerned with the expected consequences. At the basis of the collision detection algorithm lays an extensive time domain micro-simulation of vessel traffic in the given area. The Monte Carlo simulation technique is applied to obtain a meaningful prediction of the relevant factors of the collision events. Data obtained through the Automatic Identification System is analyzed in detail to obtain realistic input data for the traffic simulation: traffic routes, the number of vessels on each route, the ship departure times, main dimensions and sailing speed. The results obtained by the proposed method for the studied case of the Gulf of Finland are presented, showing reasonable agreement with registered accident and near-miss data.

Probability modelling of vessel collisions

Among engineers, risk is defined as a product of probability of the occurrence of an undesired event and the expected consequences in terms of human, economic, and environmental loss. These two components are equally important; therefore, the appropriate estimation of these values is a matter of great significance. This paper deals with one of these two components—the assessment of the probability of vessels colliding, presenting a new approach for the geometrical probability of collision estimation on the basis of maritime and aviation experience. The geometrical model that is being introduced in this paper takes into account registered vessel traffic data and generalised vessel dynamics and uses advanced statistical and optimisation methods (Monte Carlo and genetic algorithms). The results obtained from the model are compared with registered data for maritime traffic in the Gulf of Finland and a good agreement is found.

Influences of variables on ship collision probability in a Bayesian belief network model

The influences of the variables in a Bayesian belief network model for estimating the role of human factors on ship collision probability in the Gulf of Finland are studied for discovering the variables with the largest influences and for examining the validity of the network. The change in the so-called causation probability is examined while observing each state of the network variables and by utilizing sensitivity and mutual information analyses. Changing course in an encounter situation is the most influential variable in the model, followed by variables such as the Officer of the Watchs action, situation assessment, danger detection, personal condition and incapacitation. The least influential variables are the other distractions on bridge, the bridge view, maintenance routines and the officers fatigue. In general, the methods are found to agree on the order of the model variables although some disagreements arise due to slightly dissimilar approaches to the concept of variable influence. The relative values and the ranking of variables based on the values are discovered to be more valuable than the actual numerical values themselves. Although the most influential variables seem to be plausible, there are some discrepancies between the indicated influences in the model and literature. Thus, improvements are suggested to the network.

Comparison of the crashworthiness of various bottom and side structures

The purpose of this work is to compare the resistance with damage of various types of double bottom structures in a stranding event. The comparative analyses are made by use of a commercial, explicit finite element program. The ship bottom is loaded with a conical indenter with a rounded tip, which is forced laterally into the structures in different positions. The aim is to compare resistance forces, energy absorption and penetration with fracture for four different structures. Those four structures are: a conventional double bottom, a structure (presently protected through a patent) with hat-profiles stiffened bottom plating, a structure where all-steel sandwich panel is used as outer shell and a bottom structure stiffened exclusively with hat-profiles. The paper shows that it is indeed possible to elevate the crashworthiness of side and bottom structures with regards to the loading considered here without increasing the structural weight.

Bayesian network model of maritime safety management

This paper presents a model of maritime safety management and its subareas. Furthermore, the paper links the safety management to the maritime traffic safety indicated by accident involvement, incidents reported by Vessel Traffic Service and the results from Port State Control inspections. Bayesian belief networks are applied as the modeling technique and the model parameters are based on expert elicitation and learning from historical data. The results from this new application domain of a Bayesian network based expert system suggest that, although several its subareas are functioning properly, the current status of the safety management on vessels navigating in the Finnish waters has room for improvement; the probability of zero poor safety management subareas is only 0.13. Furthermore, according to the model a good IT system for the safety management is the strongest safety-management related signal of an adequate overall safety management level. If no deficiencies have been discovered during a Port State Control inspection, the adequacy of the safety management is almost twice as probable as without knowledge on the inspection history. The resulted model could be applied to performing several safety management related queries and it thus provides support for maritime safety related decision making.

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 estimating oil spill clean-up costs--a case study for the Gulf of Finland.

Existing models estimating oil spill costs at sea are based on data from the past, and they usually lack a systematic approach. This make them passive, and limits their ability to forecast the effect of the changes in the oil combating fleet or location of a spill on the oil spill costs. In this paper we make an attempt towards the development of a probabilistic and systematic model estimating the costs of clean-up operations for the Gulf of Finland. For this purpose we utilize expert knowledge along with the available data and information from literature. Then, the obtained information is combined into a framework with the use of a Bayesian Belief Networks. Due to lack of data, we validate the model by comparing its results with existing models, with which we found good agreement. We anticipate that the presented model can contribute to the cost-effective oil-combating fleet optimization for the Gulf of Finland. It can also facilitate the accident consequences estimation in the framework of formal safety assessment (FSA).

Bayesian network modeling of Port State Control inspection findings and ship accident involvement

The paper utilizes Port State Control inspection data for discovering interactions between the numbers of various types of deficiencies found on ships and between the deficiencies and ships involvement in maritime traffic accidents and incidents. Bayesian network models for describing the dependencies of the inspection results, ship age, type, flag, accident involvement, and incidents reported by the Vessel Traffic Service are learned from the Finnish Port State Control data from 2009-2011, 2004-2010 Baltic Sea accident statistics and the reported Gulf of Finland Vessel Traffic Service incidents within 2004-2008. Two alternative Bayesian network algorithms are applied to the model construction. Further, additional models including a hidden variable which represents the complete system and its safety features and which links the accident and incident involvement and Port State Control findings are presented. Based on model-data fit comparisons and 10-fold cross-validation, a constraint-based learning algorithm NPC mainly outperforms the score-based algorithm repeated hill-climbing with random restarts. For the highest scoring models, mutual information and influence of evidence analyses are conducted in order to analyze which network variables and variable states are the most influential ones for determining the accident involvement. The analyses suggest that knowledge on ship type, the Port State Control inspection type and the number of structural conditions related deficiencies are among the ones providing the most information regarding accident involvement and the true state of the hidden system safety variable.

Marine traffic risk modelling an innovative approach and a case study

This paper presents a model to analyse the risk of two common marine accidents: collision and grounding. Attention is focused on oil tankers since they pose the highest environmental risks. A case study in selected areas of the Gulf of Finland in ice-free conditions is presented. The model utilizes a formula for risk calculation that considers both the probability of an unwanted event and its consequences. The model can be decomposed into a block representation in which blocks for the probability of a collision, probability of a grounding event, and the consequences of an accident are linked. The probability of vessel colliding is assessed in terms of a minimum-distance-to-collision-based model. The model defines the collision zone using a mathematical ship motion model and considers the traffic flow to be a non-homogeneous process. Calculations are performed using data for traffic flows in the Gulf of Finland with particular attention being paid to the crossing of the channel used by scheduled ferries between Helsinki and Tallinn, and the main shipping channel. For the assessment of a grounding probability, a new approach is proposed, which utilizes a gravity-like model, where a ship and navigational obstructions are perceived as interacting objects and their repulsion is modelled by a formulation inspired by gravitational force. The considered situation in this case is the movement of oil tankers in the approach channel to an oil terminal at Sköldvik, near Helsinki. The consequences of an accident are expressed in monetary terms, and concern the costs of cleaning up an oil spill, based on the statistics of compensation levels claimed from the International Oil Pollution Compensation Fund.