Modelling Marine Accident Frequency

Transcription

1 Aalto University School of Science and Technology Faculty of Information and Natural Sciences Jutta Ylitalo Modelling Marine Accident Frequency Master s thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Technology in the Degree Programme in Engineering Physics. Espoo, February 2, 2010 Supervisor: Professor Harri Ehtamo Instructors: Professor Pentti Kuala and PhD Jakub Montewka

2 Aalto University School of Science and Technology Faculty of Information and Natural Sciences ABSTRACT OF THE MASTER S THESIS Author: Jutta Ylitalo Title: Modelling Marine Accident Frequency Title in Finnish: Laivaonnettomuuksien todennäköisyyksien mallintaminen Degree Programme: Degree Programme in Engineering Physics Maor subect (name and code): Systems and Operations Research Mat-2 Minor subect (name and code): Strategy and International Business TU-91 Chair (name and code): Applied Mathematics Mat-2 Supervisor: Professor Harri Ehtamo Instructors: Professor Pentti Kuala and PhD Jakub Montewka Abstract: In this thesis, the author concentrates on marine accident frequency models and on the analytical estimation of accident frequency in the Gulf of Finland. The Gulf of Finland is a sensitive sea area and a part of Baltic Sea. Automatic Identification System (AIS) data of 2008 is used in the calculations of ship-ship collision and grounding frequency. In addition, an estimate of collision frequency in the Gulf of Finland in 2015 is made based on a future traffic scenario. The focus is on the number of accident candidates. Causation factors, which quantify the probability of not making successful evasive manoeuvres while being on an accident course, are taken from earlier work. IWRAP Mk2 program is utilized to make the calculations. Ice conditions are not considered in the analysis. The results imply that the main shipping route through the Gulf of Finland is the most prone to ship-ship collisions. Especially its eastern part seems risky. The obtained ship-ship collision frequency, 0.26 per year, was in line with collision statistics but the result was found to be rather sensitive to the values of input parameters. An estimate made for 2015 showed a raise of 164 % of the number of ship-ship collision candidates compared to The yearly grounding frequency of 0.31 was obtained for the analysed area at west side of Kotka. The analysis did not reveal particularly risky locations. The result was reasonable in the light of grounding statistics but the model was found to be sensitive to parameter values that could not be defined in sufficient detail. Date: February 2, 2010 Language: English Number of pages: 91 Keywords: Ship, collision, grounding, marine accident modelling, Gulf of Finland, IWRAP Mk2 2

3 Aalto-yliopisto Teknillinen korkeakoulu Informaatio- a luonnontieteiden tiedekunta DIPLOMITYÖN TIIVISTELMÄ Tekiä: Jutta Ylitalo Työn nimi: Laivaonnettomuuksien todennäköisyyksien mallintaminen Title in English: Modelling Marine Accident Frequency Koulutus-ohelma: Teknillisen fysiikan koulutusohelma Pääaineen koodi a nimi: Mat-2 Systeemi- a operaatiotutkimus Sivuaineen koodi a nimi: TU-91 Yritystrategia a kansainvälinen liiketoiminta Opetusyksikön (ent. professuuri) koodi a nimi: Mat-2 Sovellettu matematiikka Työn valvoa: Professori Harri Ehtamo Työn ohaaat: Professori Pentti Kuala a PhD Jakub Montewka Tiivistelmä: Tässä diplomityössä tekiä keskittyy laivaonnettomuuksien todennäköisyysmalleihin a analyyttiseen Suomenlahden onnettomuustodennäköisyyden arviointiin. Suomenlahti on herkkä merialue Itämerellä. Vuoden 2008 Automatic Identification System (AIS)-tietoa käytetään laskettaessa laivoen yhteentörmäysa karilleaotodennäköisyyttä. Lisäksi tehdään liikenteen kasvuarvion avulla arvio laivoen yhteentörmäystodennäköisyydestä Suomenlahdella vuonna Tekiä keskittyy työssä onnettomuuskandidaattien määrään. Tarvittavat aiheutumistodennäköisyydet otetaan aiemmista töistä. Aiheutumistodennäköisyys määrittää todennäköisyyden onnistuneen väistöliikkeen tekemiselle. Laskemiseen käytetään IWRAP Mk2-ohelmaa. Jääolosuhteiden vaikutusta ei ole huomioitu työssä. Tuloksista ilmenee, että laivoen yhteentörmäystodennäköisyys on suurin itä-länsi-suuntaisella pääväylällä Suomenlahden läpi. Erityisesti väylän itäiset osat ovat yhteentörmäysalttiita. Tuloksena saatu laivoen yhteentörmäystodennäköisyys, 0,26 vuodessa, on linassa onnettomuustilastoen kanssa, mutta herkkyysanalyysin perusteella tulos on herkkä käytettyen parametrien muutoksille. Vuodelle 2015 tehty arvio osoittaa yhteentörmäystodennäköisyyden nousevan 164 % verrattuna vuoteen Analyysialueelle Kotkan länsipuolella saatiin vuosittaiseksi karilleaotodennäköisyydeksi 0,31. Analyysi ei palastanut erityisen riskialttiita paikkoa alueella. Tulos on ärkevä verrattuna karilleaotilastoihin, mutta se on herkkä parametrien vaihtelulle eikä parametrea kyetty määrittämään riittävällä tarkkuudella. Päivämäärä: Kieli: Englanti Sivumäärä: 91 Avainsanat: Laiva, yhteentörmäys, karilleao, laivaonnettomuuksien mallintaminen, Suomenlahti, IWRAP Mk2 3

4 Preface This thesis was made for the unit of Marine Technology at the School of Science and Technology of Aalto University. The work was partly financed by SAFGOF proect, Evaluation of the traffic increase in the Gulf of Finland during the years and the effect of the increase on the environment and traffic chain activities, of Kotka Maritime Research Centre. First, I want to thank my instructor Pentti Kuala for giving me the opportunity to make my master s thesis on this topic. I am grateful to both of my instructors, Pentti Kuala and Jakub Montewka, for giving me many valuable comments on my work. I thank Harri Ehtamo for supervising my thesis. I owe to Erik Sonne Ravn from Danish Maritime Safety Administration and Markus Porthin, Tapio Nyman, and Robin Berglund from VTT for their help in getting and partly processing the AIS data I needed for the analysis. I want to express my gratitude also to Jenni Kuronen from University of Turku for evaluating detailed information for my needs and about helping to find solutions to questions related to traffic estimates of I am thankful to Maria Hänninen and Arsham Mazaheri for our cooperation and to Floris Goerlandt for inspiring conversations about the topic and for his comments on this thesis. Finally, I want to thank all my family and friends for being there for me. Especially, I am grateful to my husband Jukka for always supporting and encouraging me along the thesis process and to my lovely daughter Viola for constantly reminding me about what is actually important in life. Espoo, January 15, 2010 Jutta Ylitalo 4

5 Contents LIST OF ABBREVIATIONS INTRODUCTION Motivation The Gulf of Finland Concepts of Risk and Risk Analysis Limitations Structure of the Thesis MARINE ACCIDENT MODELLING Introduction Modelling the Frequency of Marine Accidents Accident Types Collision Frequency Models Pedersen s Model Model of Fowler and Sørgård Macduff s Model COWI s Model Ship-Ship Collision Frequency Model Used in IWRAP Grounding Frequency Models Pedersen s Model Simonsen s Model COWI s Model The Model of Fowler and Sørgård Grounding Frequency Model used in IWRAP Causation Factor RESEARCH METHODS AND MATERIAL Research Methods Introduction to Data Analysed Areas Area of Collision Frequency Analysis Area of Grounding Frequency Analysis Ship Traffic in the Gulf of Finland in

6 3.4.1 Traffic Image Ice Season Ship Traffic Arriving to the Gulf of Finland and Leaving It Ship Traffic between Helsinki and Tallinn Ship Traffic at East Side of Gogland Ship Traffic at west side of Kotka Ship Traffic to Sköldvik Ship Traffic in the Gulf of Finland in IWRAP MK Introduction Discussion on Collision Frequency Models Discussion on Grounding Frequency Models IWRAP and the Gulf of Finland RESULTS Collision Candidates in the Gulf of Finland in Yearly Collision Candidates Monthly Collision Candidates Sensitivity Analysis Collision Statistics Discussion Grounding Candidates in the Gulf of Finland in Results Sensitivity Analysis Grounding Statistics Discussion Collision Candidates in the Gulf of Finland in Traffic Multipliers Collision Candidates Discussion CONCLUSIONS FUTURE RESEARCH TOPICS Accident Frequency Models of IWRAP Collision Frequency Modelling in Winter Future Ship Traffic Estimates

7 REFERENCES...82 APPENDIX A: DEVELOPMENT SUGGESTIONS FOR IWRAP

8 List of Abbreviations AIS FMA IALA IMO MMSI IWRAP SAFGOF Automatic Identification System Finnish Maritime Administration International Association of Marine Aids to Navigation and Lighthouse Authorities International Maritime Organization Maritime Mobile Service Identity IWRAP Mk2 that is IALA Waterway Risk Assessment Program, software for making ship-ship collision and grounding frequency calculations A research proect: Evaluation of the traffic increase in the Gulf of Finland during the years and the effect of the increase on the environment and traffic chain activities 8

9 1 Introduction 1.1 Motivation Traditional and the simplest way to estimate the probability of marine accidents is to contemplate accident statistics. However, the scarcity of accident statistics causes limitations, e.g., if no accidents have occurred in a specific highly sensitive area, it does not mean that the probability of an accident in that area would be zero. Obviously, statistics describe only the past but not the future. An essential problem with accident statistics is the lack of standard procedure to store accident information. For example, accidents are classified in alternative ways in different databases. In addition, it is impossible to estimate how, e.g., new safety measures would change the risk level based on the analysis of statistics. In practise, analytical risk evaluation or expert estimation are the only ways to assess the impact on the risk level when regulations, traffic volume, ship characteristics or shipping lanes change. For example, analytically it is possible to give an estimate how ship-ship collision frequency would change if the location of a shipping route was changed. (Pedersen 1995) The obective of this thesis is to estimate the frequency of the most important marine accidents, ship-ship collision and grounding, in the Gulf of Finland. Several studies concerning the frequency of marine accidents in the Gulf of Finland have been published (Rosqvist et al. 2002, Ylitalo et al. 2008, Kuala et al. 2009, Montewka et al. 2009) but none of them has covered as large area of the gulf as this thesis. In addition, a study is made to estimate the impact on collision frequency of the most probable traffic scenario for The Gulf of Finland As a part of Baltic Sea, the Gulf of Finland is classified as a Particularly Sensitive Sea Area by IMO (2005). Ship traffic in the gulf has increased fast in the beginning of the 21st century. Traffic is estimated to most likely increase also by % of cargoes transported in the Gulf of Finland in 2007 were loaded or unloaded in Russian ports. The number reflects the fact that the maor part of the cargo traffic moves through the Gulf of Finland to Russia or from Russia. (Kuronen et al. 2008) 9

10 Navigational conditions have substantially changed in the Gulf of Finland in the beginning of the 21 st century. Ship traffic has increased remarkably but also new safety measures have been adopted. For example, GOFREP and Automatic Identification System (AIS) have been introduced. GOFREP is the Mandatory Ship Reporting System of the Gulf of Finland. All ships of 300 gross tonnage and upwards are required to make a radio report to the Vessel Traffic Service (VTS) when entering the gulf or leaving a port in the gulf. The system has been launched to improve safety of navigation and to protect the marine environment of the gulf. Automatic Identification System is a means for VTS and ships to exchange information automatically by radio (see Chapter 3.2). (FMA 2009) 1.3 Concepts of Risk and Risk Analysis A measure of potential loss is called risk. It is defined as the product of the probability or frequency of the unwanted event and its consequences if it occurs: risk = probability of the event its consequences In this thesis, the probability is estimated for a defined time period, e. g., the probability of a ship-ship collision during a year or the frequency of groundings. The consequences can be measured, for example, as lost human lives or the costs due to an oil spill. Risk analysis is about what may happen, how probable the occurrence is and what the consequences are. (Bedford & Cooke 2001, Kristiansen 2005, Ayyub 2003, Modarres 2006) 1.4 Limitations This thesis is about modelling and estimating marine accident frequencies. Thus, consequences of accidents are not considered. Moreover, causation factors (see Chapter for a definition) are not analysed in detail. However, causation factors and causes affecting to its value in different conditions are argued in Chapter 2.4. The focus is on estimating and modelling the number of so-called geometrical collision candidates (see Chapter 2.1.1). In practice, it is not possible to present all available accident models. Therefore, only the models considered the most meaningful are presented and compared to the models IWRAP uses. IWRAP is software for calculating estimates of ship-ship collision and grounding frequencies. 10

11 The area of interest is limited to the Gulf of Finland. Most of the gulf is covered in ship-ship collision frequency study (see Figure 13 and Figure 14) and waterways approaching Kotka from west are included in grounding frequency calculations (see Figure 15). This thesis is concentrated in ice-free period. Winter time is considered as prolonged autumn because the case study covers the year 2008 and the ice season was extremely mild: the maximum ice cover, km 2, was the smallest since the gathering of ice statistics was started (Baltic Sea Portal 2008). Another reason for limiting ice season of the scope is that no specific accident frequency models exist for the navigation in ice. The effect of small vessels to the ship-ship collision and grounding frequencies is excluded from the analysis. Information about the number of pleasure boats and other small vessels is not easily available. In addition, in IWRAP it is only possible to take into account small ships of which positions are uniformly distributed to the analysis area around the year. That starting point does not fit well to the Gulf of Finland where ice season is long and pleasure boats are concentrated on specific areas (Hänninen et al. 2002). While making estimates for the year 2015, some simplifications had to be made. The possible changes in waterway locations are not taken into consideration. The average ship size is assumed to remain the same than it was in Only the increase of traffic volume is considered and all other factors have been left without attention. The analysis of the effect of new possible safety regulations is left to future research. 1.5 Structure of the Thesis First, Chapter 2 is about presenting existing accident frequency models. Research methods and material are presented in Chapter 3. IWRAP and the models it utilizes are discussed in Chapter 4. The results are presented, compared to statistics, and discussed in Chapter 5. Finally, in Chapter 6 the thesis is concluded and possible future research topics are introduced in Chapter 7. 11

12 2 Marine Accident Modelling 2.1 Introduction Modelling the Frequency of Marine Accidents Typically, marine accident probabilities are modelled based on the work of Fuii et al. (1974) and Macduff (1974). Following their first ideas, the frequency of marine accidents is generally estimated as F = N a P C (1) where N a is the number of accident candidates and P C is the causation factor. Accident candidates are the ships that are on an accident course in the vicinity of a ground or another vessel. In other words, the number of accidents would be N a if no evasive manoeuvres were ever made to avoid the accident. Causation factor is the probability of failing to avoid the accident while being on an accident course. It quantifies the fraction of accident candidates that are actually grounding or colliding with another vessel Accident Types The two most important accident types in the Gulf of Finland between and have been grounding (47.6 %) and ship-ship collision (20.0 %) according to Figure 1 (Kuala et al. 2009). They are also the only accident types discussed in this thesis. Other accident types include, e.g., machinery damages, fire and collisions with a floating obect, a bridge, or a quay. Grounding can be defined as the ship s impact with the shoreline or individual shoal (Kristiansen 2005, Mazaheri 2009). Two types of groundings exist: powered groundings and drifting groundings. The reason for a powered grounding is a navigational error (Fowler & Sørgård 2000). A mechanical failure such as engine breakdown can lead to a situation when the ship cannot navigate and thus drifts. The 12

13 drifting vessel may end up grounding if wind or current carries it towards a shoal or a ground (Fowler & Sørgård 2000). Figure 1. Number of accidents by accident type in the Gulf of Finland. (Kuala et al. 2009) Ship-ship collision occurs if a ship strikes another ship (Kristiansen 2005). Since other collision types, e.g., collision with a floating obect, are not considered in this thesis, ship-ship collisions are referred as collisio ns hereafter. Collisions can be divided into head-on (Figure 2), overtaking (Figure 3), merging collisions (Figure 4), crossing (Figure 5) and bend collisions (Figure 6). A bend collision may occur if a ship does not turn at a bend of a waterway. Contrary to the case of grounding, it is not essential to divide collisions into powered and drifting collisions as in a collision situation there are two ships involved and it is enough that one of the ships is able to avoid the other. However, the possibility of a mechanical failure should be considered when estimating causation factor for collisions because it reduces the probability of being able to avoid the other vessel. Figure 2. Head-on collision. Figure 3. Overtaking collision. Figure 4. Merging collision. 13

14 Figure 5. Crossing collision. Figure 6. Bend collision. 2.2 Collision Frequency Models Pedersen s Model Pedersen (1995) considers the crossing of two waterways that is illustrated in Figure 7. When two ships, i and, approach the crossing as in Figure 7, their relative velocity is V i (1) 2 (2) 2 (1) (2) ( V ) + ( V ) 2V V cosθ = (2) i i where (1) V i is the velocity of the ship i on waterway 1, (2) V is the velocity of the ship on waterway 2, and θ is the angle between the vessel courses as shown in Figure 7. Figure 7: Crossing waterways. 14

15 Ships are grouped by their type and length in order to utilize the different characteristics of vessel groups. For example, the average speed varies significantly from one ship group to another. The manoeuvrability of the ship of different ship classes varies too and separate causation factors can be defined for groups. The geometrical collision diameter of ships is calculated as if ships were rectangular as seen from Figure 8. The equation of the geometrical collision diameter of ships i and is D i + B = (1) i L (1) i V (2) + L V i (2) V 1 sinθ V (2) i V (1) i sinθ + B ½ 2 (2) 1 V sinθ V (1) i i ½ 2 (3) where (1) L i is the length of the ship i in waterway 1, (2) L is the length of the ship in waterway 2, and B is the width of vessel identified by similar notation. Figure 8: Definition of geometrical collision diameter. (Pedersen 1995) Pedersen (1995) utilizes the idea of Fuii (1983), that in a segment dz of the waterway 2, the number of ships belonging to class on collision course with one ship of the class i during the time Δt is 15

16 Q V (2) (2) f (2) ( z ) D V dz Δt i i i (4) where (2) Q is the number of movements of ship class in waterway 2 per (2) z considered time period Δ t, f ( ) is the distribution of traffic of the ship class across the waterway 2, and z is the distance from the centreline of the waterway 2. If the normal distribution is used to describe the traffic distribution across the waterway, where f (2) ( z (2) μ is the mean value of z and ) (2) 2 ( z ) ( ) μi (2) 2 σ 1 exp (2) σ 2 π (5) = 2 (2) σ is the standard deviation of z. Another distribution that is commonly used to describe traffic spread across waterways in accident frequency calculations is the uniform distribution (Ang & Tang 1975): ( 2) b a, a z b f ( z ) = (6) 0, elsewhere where a is the lower bound and b is the upper bound. Often, a combination of normal and uniform distributions is used to describe the traffic distribution across a waterway (e.g. Randrup-Thomsen et al. 2001). In the Pedersen s model (1995), the number of class i ships on the considered waterway segment is 16

17 Q V (1) i (1) i f (1) i ( z ) dz i i (7) where notation is as in equation (4). The number of collision candidates N a is obtained by multiplying the expressions (4) and (7), integrating over the considered area, and summing up the different ship classes on each waterway: N a = Q ( z, ) Vi i Ω i z (1) i (1) Q V (2) (2) f (1) i ( z ) f i (2) ( z ) V D daδt i i (8) To get the collision frequency, the number of collision candidates needs to be multiplied by an appropriate causation factor. For parallel waterways, the equation (8) yields N a = π L 2 w i Q V (1) i (1) i Q V (2) (2) ( V (1) i + V (2) ) ( B (1) i + B (2) 1 2 ) exp( μ σ σ ) Δt (9) where μ is the distance between the average positions of ships moving to opposite directions as Figure 9 presents. Figure 9. Head-on collision Model of Fowler and Sørgård Fowler & Sørgård (2000) suggest that the frequency of critical situations, n co, is calculated assuming that traffic movements are uncorrelated. A critical situation denotes that two ships are crossing within half a nautical mile from each other. Encounter frequency is estimated by a pair-wise summation across all shipping lanes at the considered location. However, Fowler & Sørgård (2000) do not present a practical procedure to calculate the number of critical situations. In the model, the frequency of encounters is multiplied by the probability of a collision per encounter, p co, to get the collision frequency, f co. Calculation may be done to all vessels or only to some specific ship types. Collision frequency, f co, of the studied location is thus determined as 17

18 f = n + co co ( pc Pco, c p f Pco, f ) (10) where p c, p f are the probabilities of clear and reduced visibility and P co c Pco, f,, are the causation factors in clear and reduced visibility Macduff s Model Macduff (1974) builds his model on molecular collision theory. Ships on a shipping lane are regarded as a homogenous group: they are navigating at the same speed and they have similar dimensions. Macduff contemplates a vessel that approaches a shipping lane on a course that makes an angle θ with the lane. Inspired by molecular theory, he defines mean free path of a ship as the distance which the ship can proceed, on average, before colliding with one of the vessels navigating along the shipping lane. This train of thought leads to the geometrical collision probability of XL sin( θ 2) P C = 2 D 925 (11) where D X L is the average distance between ships (density measure, in miles), is the actual length of path to be considered for the single ship (nautical miles), and is the average vessel length (metres). Macduff s model does not cover other collision types than crossing collisions COWI s Model COWI (2008) divides collisions into crossing collisions and parallel collisions which include head-on and overtaking collisions. Parallel Collision Model Meeting frequency of two ships is defined as: P T V V 1 2 = LN1N 2 V1V (12) 2 where 18

19 L is the length of the considered waterway, N, N 1 2 are the yearly numbers of passings of ship 1 and ship 2, and V,V 1 2 are vessel speeds. Geometrical collision probability is calculated as: where B1 B = + (13) c P G 2 c is the width of the waterway segment and B, B 1 2 are the breadths of the vessels in question. COWI (2008) admits that it is also possible to collide with a ship navigating in a neighbouring width segment. However, that case is neglected in their analysis as width segments they have used are significantly larger than the average vessel breadth. The total parallel collision probability of ships, P X, is calculated by summing the collision probability of each pair of two ships on each width segment of the waterway: P = P P X T G P C k RR (14) where P T P G is the yearly frequency of ships meeting in one segment of the waterway, is the geometrical collision probability, P C is the causation factor, and k RR is the risk reduction factor. A risk reduction factor is used in order not to change causation factor itself due to different factors reducing the probability of collision. If such factors as pilotage, local experience, or increased safety standards are affecting, the risk reduction factor of k = 0.75 is used. If both ships are under the influence of such a feature, k = RR Ferries sailing frequently in the analysed area are considered to act like if they had a RR 19

20 pilot onboard. The main reason for this is that the crews have much experience from the area. Crossing Collision Model COWI (2008) defines crossing collision as a collision that includes ships sailing along different waterways. Two ships can theoretically collide if their traces intersect. Traces can intersect when two waterways cross each other (X-crossing) or part of the traffic of a waterway may merge to the traffic of another waterway (Y-crossing). For X-crossings, the probability that the traces of ships on different waterways intersect is 1, so P = 1. For Y-crossings, P = I I According to COWI (2008), the possibility of a collision between two ships navigating at intersecting routes can be expressed by critical time interval critical time interval is illustrated in Figure 10 and determined as Δ t. The 1 V2 V1 V1 V2 Δt = B2 + B1 + L1 V2 + L2 V1 V1V 2 sinθ tanθ sinθ tanθ (15) where L, L 1 2 are vessel lengths, and θ is the angle between two crossing waterways as shown in Figure 10. Figure 10. Critical time interval in a crossing collision. (COWI 2008) In the model, the passage of ships on waterways is assumed to be a Poisson process. The geometrical collision frequency is calculated as 20

21 P G N2 Δt = N ( 1 e N N Δt. (16) 1 ) 1 2 Thus, yearly collision frequency is determined as P = P P X I G P C k RR (17) where P C is the causation factor and k RR is the risk reduction factor. The value of risk reduction factor is chosen in similar way as in the case of parallel collision. Crossing Collisions with Small Vessels COWI (2008) also pays attention to the probability of collision involving fishing boats or yachts. The probability of these collisions cannot be included in calculations described above as small vessels often do not carry AIS transmitters and do not sail on normal waterways. For example, sailing yachts frequently change course and may move at low speed. In addition, most small vessels do not move from one port to another but return to the departure port. Fishing boats are assumed to sail to a fishing ground and remain there during several hours moving only at slow speed. Also yachts are assumed to cross the main traffic route in its area twice during one sailing session. A typical small vessel is assumed to be 12 m long and 4 m wide. Collision frequency is calculated while taking into consideration the size of small vessels Ship-Ship Collision Frequency Model Used in IWRAP The following collision types are considered in IWRAP: overtaking, head-on, crossing, merging, and bend collisions. The frequency of overtaking and head-on collisions is dependent on the distribution of traffic across the waterway contrary to the frequency of other kind of collisions. All numbers of collision candidates have to be multiplied by a suitable causation factor. (Friis Hansen 2008) Head-on Collisions The relative speed of two ships approaching each other is expressed as 21

22 (1) i (2) V i = V + V (18) where (β ) V α is the speed of the ship of the ship class α moving in direction β. The number of collision candidates for head-on collisions on a waterway is evaluated as N G ( head on) = L w i, P i (1) (2) Gi, ( head on) ( Qi Q (1) (2) ) Vi V V (19) where L w is the length of the waterway, Gi P, is the probability that two ships of ship classes i and collide in a headon meeting situation if no evasive manoeuvres are made, and (β ) Q α is the number of passages per time unit for ship class α moving in direction β. P, depends on traffic distributions across the waterway, f i ( y) and f ( y). Gi Typically, traffic spread across a waterway is defined by a normal distribution but any distribution may be used. Distributions have to be assumed to be independent. When traffic is normally distributed with parameters (μ (1) i, σ (1) i ) and (μ (2), σ (2) ), the mean sailing distance between vessels heading to opposite directions is (1) (2) = (20) μ μ i + μ (1) (2) and the standard deviation of the oint traffic distribution is i (1) 2 (2) ( σ ) ( σ ) 2 σ = +. (21) i In the case of normal distribution, P, can be calculated as Gi Bi μ Bi + μ P Φ Gi, ( head on) = Φ σ i σ (22) i where Φ ( ) is the standard normal distribution function and B i is the average vessel breadth: 22

23 B i (1) (2) Bi + B = (23) 2 where (β ) B α is the average breadth of vessel of the ship class α on the waterway β. Overtaking Collisions When estimating the number of overtaking collisions, the relative speed in equation (18) is replaced by V i (1) i (2) = V V (24) where V > 0 because otherwise no overtaking may occur. The geometric probability i of meeting (equation 19) is replaced by P Gi, (1) (1) (1) Bi + B ( overtaking) = P yi y < 2 (1) (1) (2) Bi + B P yi y < 2 (1) For normally distributed traffic, μ in equation (20) is now (1). (25) (1) (1) μ = μ i μ. (26) Thus, the number of overtaking collision candidates is calculated as in the case of head-on collision (equation (19)). (Friis Hansen 2008) Crossing Collisions In IWRAP, the crossing collision situation is considered as in Figure 7. The relative speed of vessels V i and the geometrical collision diameter D i are calculated as in Pedersen s model, see equations (2) and (3) and Figure 8. It is also noted that for practical reasons, the crossing angle θ has to be limited to an interval of 10 to 170 degrees as if it goes to zero, the length of the crossing goes to infinity. IWRAP also uses apparent geometrical collision diameters seen from different vessels to estimate the probability of ships to be struck or striking vessels if a collision occurs. The number of crossing collision candidates is defined as (Friis Hansen 2008) 23

24 N G ( crossing ) = i, Q V (1) i (1) i Q V (2) (2) D V i i 1 sinθ, θ 0. (27) Merging and Bend Collisions Merging collision is considered as crossing collision but the ship tracks have only the probability of 0.5 to intersect. A bend collision may occur if a ship does not turn at a bend of a waterway and as a result is on a collision course with another vessel. The probability of omission is taken as (Friis Hansen 2008) Crossing Collisions with Small Vessels In IWRAP, it is possible to include also the small vessels that do not carry AIS equipment by inserting area traffic. However, area traffic is assumed to be uniformly distributed to the analysis area and around the year. 2.3 Grounding Frequency Models In this thesis, only the most important grounding frequency models are presented and compared to the model IWRAP uses. Namely, the models of Pedersen (1995), Simonsen (1997), COWI (2008), and Fowler and Sørgård (2000) are introduced. Other models are left out. For example, Macduff (1974) considers grounding expecting random navigation in a narrow channel of constant width. The approach is not suitable to the extensive analysis of the Gulf of Finland. However, it may be possible to apply the idea of narrow channel to ice channels. A complete state-of-art report about existing grounding frequency models is written by Mazaheri (2009) Pedersen s Model Pedersen (1995) considers the situation that Figure 11 presents. His approach to grounding frequency is similar to his approach to collision frequency that was presented in Chapter Pedersen estimates the number of groundings as a sum of four following accident categories: I. Ships navigating along the waterway at normal speed. A grounding may occur because of a human error or problems with the propulsion or steering system near the shoal. 24

25 II. Ships which do not change course as they should at the bend and ground on the shoal as a result. III. Ships which have to take an evasive action near the shoal and therefore ends up grounding on the shoal. IV. All other occasions, e.g., drifting ships and off-course ships. Figure 11: Bend of a waterway. (Pedersen 1995) Grounding candidates of categories I and II are shown in Figure 11. For the mentioned categories, simplified formulas to calculate expected number of groundings per year (F) are: where F cat n cat. I = PciQi f i ( z) Bidz (28) ship class, i L F n ( d ai ) ai 0 f i ( z) L. II = PciQi P Bidz (29) ship class, i i P ci is the number of ship class determined by vessel type and dead weight tonnage or length, is the causation factor or the probability of failing to avoid a shoal on the navigation course, depending on ship class i, 25

26 Q i is the number of movements per year of vessel class i in the considered lane, L z is the total width of the studied area perpendicular to vessel traffic, is the coordinate perpendicular to the route, f i (z) is the ship traffic distribution across the waterway, B i P 0 d a i is a grounding indication function which is one when the ship would ground on the shoal without evasive manoeuvres and zero when the ship would not, that is would pass the area safely, is the probability of omission to check the position of ship, is the distance from the bend to the shoal, varying with the position of the ship across the waterway, and is the average length between position checks by the navigator. According to Pedersen (1995), mathematical expressions like equations (28) and (29) generally manage to estimate the probability of grounding with reasonable accuracy Simonsen s Model The model of Simonsen (1997) is a little more developed version of the Pedersen s model. He assumes that the event of checking the position of the ship can be described as a Poisson process. The equation (28) remains the same but with abovementioned assumption the equation (29) becomes F cat II = ship class, i P Q e ci i d ai L f z B dz i ( ) i (30) where the factor d ai e represents the probability of omission to check the position from the bend to the shoal. Other notation is as in the equation (29) COWI s Model COWI (2008) divides groundings into three groups: groundings due to imprecise navigation, groundings due to missed turns, and drifting groundings. However, drifting groundings are not included in their analysis. In the report of COWI, it is reminded that islands appear as a line on the horizon rather than a round obect when seen from a ship. Therefore it is suggested to model 26

27 grounds and sub-sea grounds by determining their linear proections. A ship will run aground if it continues on a straight course towards a proection. Groundings Due to Imprecise Navigation According to COWI (2008), groundings due to imprecise navigation tend to occur in difficult waters when the crew may not be fully aware of the ship s position. Little experience of local conditions, bad navigational equipment, or lack of training can be the reason. COWI (2008) recommends analyzing the number of geometrical collision candidates P G a few nautical miles before they would run aground if they did not change course. Ships approaching an island are illustrated in Figure 12. Figure 12. Ship traffic approaching a ground. (COWI 2008) The number of collision candidates is dependent on the distance between the observation point of traffic distribution across the waterway and the potential grounding location. The shorter the distance is the more ships have already corrected their course to avoid the ground. Due to those avoiding manoeuvres, the observed number of grounding candidates diminishes as the potential grounding location is approached. A distance of 6-29 nm is recommended meaning a travel time from half an hour to one hour. COWI (2008) also presents a calibrated distance factor: where x k DC = d (31) 27

28 x = 10 nm = km and d = the distance from the observation point to the ground. Like in the collision model, a risk reduction factor is used to model the effect of pilotage, local experience, and safety standards. If one of the previously mentioned is affecting, a reduction factor of k = 0. 5 is used to multiply the grounding probability. Finally, the yearly grounding frequency is obtained: RR P = NP X G P C k DC k RR (32) where N is the yearly number of ship movements on the considered waterway. Groundings Due to Missed Turns A bend on a waterway may be missed especially in open sea where it is in general easy to sail and the crew does not have to be alert as not much of their attention is needed. If a ground or a shoreline is located after the bend, missing the turn may cause the ship to run aground. The frequency of grounding in this way is calculated as P = NP X NT P G P C k RR (33) where P NT is the frequency at which ships miss the turn and do not turn at a later but sufficiently early point so that no danger of grounding occurs. According to COWI (2008), the value of P NT can be calculated as x λ V P NT = e (34) where λ x V is check frequency (0.5-1 minutes), is the distance between the bend of the waterway and the ground, and is the speed of the vessel. P G and k RR are calculated in the same way than in the case of groundings due to imprecise navigation. 28

29 The approach is calibrated to narrow channels, such as Drogden in Øresund. A check frequency of minutes is considered as characteristic in a normal situation. In locations with plenty of space to navigate, a grounding may occur only if the crew is lacking attention for an abnormally long period. Still, COWI (2008) used the above described model as an accepted state-of-the-art model The Model of Fowler and Sørgård Fowler and Sørgård (2000) use different models for powered and drifting grounding. Powered Grounding The main reason for powered grounding is failure to make a critical course change in the vicinity of the shoreline or shallow water. The dominant critical situation n pg is defined as when a ship navigates to a way-point within 20 minutes of landfall such that a powered grounding results if no critical course change is made. The frequency of powered groundings f pg is given by pg pg ( Pc p pg, c Pf p pg, f f = n + ) (35) where P c and P f are the probabilities of clear and reduced visibility, and p pg, c and pg f p, are the corresponding probabilities of grounding given clear or reduced visibility, practically like causation factors. Drifting Grounding A drifting grounding occurs if a ship loses the ability to navigate due to steering or engine failure and then wind and currents force it towards ground. Number of shiphours spent within 50 nautical miles of the shoreline defines the critical situation for drifting grounding. Control of drifting vessels can be regained by repair, emergency towage or anchoring. If a ship is drifting towards shoreline and control is not regained, the ship runs aground. Tug model contains among other things the following parameters: tug availability, response time, speed, time to connect a line and exert a controlling influence on the ship, and performance as a function of wind speed, location and sea-state. Anchoring is only possible when there is sufficiently space. The sufficient length of suitable water can be determined exactly or 0.5 nautical miles 29

30 can be used as a slightly conservative value. If enough suitable water exists, the probability that the anchor holds is calculated as a function of wind speed and the type of sea bottom. The overall drifting grounding frequency f dg is given by where f l pd pw[ ( psr w )( pt w )( pa w )] p,, 1, 1 f dg = 1, (36) l w f p, l is the number of ship breakdowns per year in the lane l, p d is the probability that a drifting ship moves towards shore, w p w indicates that the following parameters depend on wind speeds, is the probability of a wind speed category, p, is the probability that a drifting ship is saved by self repair prior to sr w t w grounding, p, is the probability that a drifting ship is saved by tugs prior to grounding, and p a is the probability that a drifting ship is saved by anchoring prior to grounding. For their case study covering the North Sea area, Fowler and Sørgård (2000) have categorized ships by type: tankers, general cargo ships, bulk ships and ferries. The agreement between the powered grounding model and historical data was good for tankers (factor of 1.01) and bulk carriers (factor of 0.51), reasonable for general cargo ships (factor of 0.48), and poor for ferries (185). Fowler and Sørgård estimate the discrepancy in case of ferries to be related to grounding probability, given a critical situation. The agreement between drifting grounding model and historical data was good for ferries (factor of 0.76) but grounding frequencies were too high for other ship types (factor of 18.6 for tankers, 16.3 for general cargo ships, and 11.5 for bulk carriers). 30

31 2.3.5 Grounding Frequency Model used in IWRAP IWRAP uses models very close to Pedersen models of collision and grounding. Ship categories I-IV are considered as in Pedersen s model (see Chapter 2.3.1). Groundings of ship categories I and II are regarded as powered groundings and groundings of categories III and IV as drifting groundings. (Friis Hansen 2008) Powered Groundings In IWRAP, grounding frequency of ship categories I and II is evaluated with expressions presented by Pedersen (1995) and Simonsen (1997). The number of grounding candidates of ship category I is calculated as in the equation (28) of Pedersen s model. For the ship category II, the equation (30) of Simonsen s model is used in the case the ground is orthogonal to the sailing route. In other cases, the equation is adapted to the inclination. Average distance between position checks by the navigator, a i, is proposed to be a function of ship speed: a i = λv (37) where λ V is time between position checks and is ship speed. Friis Hansen (2008) also presents a more complicated expression for the case when the ground is not perpendicular to the sailing direction. Drifting Groundings The most usual reasons for ships to drift are considered to be rudder stuck and blackout of the main engine. A blackout may occur at any location of the ship track. The user may define a probability distribution of having wind coming from each direction. The ship is supposed to drift approximately to wind direction which affects the locations where drifting ships are assumed to ground. Ships may recover by repairing the problem, by anchoring or by calling a tug boat. (Friis Hansen 2008, IALA 2009b) The occurrence of blackouts is assumed to be a Poisson process. Thus, the probability of having a blackout on a waterway segment of length L is (IALA 2009b): 31

32 P blackout Lsegment ( L = segment ) 1 exp λblackout (38) vvessel where λ is the blackout frequency and v vessel is the speed of the vessel. The default blackout frequency is assumed to be 0.1 per year for passenger vessels and ro-ro vessels and 0.75 for all other ship types. The drifting speed v drift is uniformly distributed with the lower bound of 1 m/s and the upper bound of 3 m/s. f v ) is the probability density function of the drifting speed. ( drift The time to grounding is defined as d ground t ground = (39) v drift where d ground is the distance to ground. The default repair time distribution is Weibull distributed with scale parameter a = 1.05 and b = 0.9 which leads to the probability of no repair P no repair to be defined from F no repair 0.9 ( t) = exp( 1.05t ) (40) The probability of no anchoring is 1 in the current version of IWRAP because the anchoring probability has not been implemented yet. The number of drifting groundings is calculated as N drift grounding P = N no anchoring ( t 360 Pwind ( ψ ) Pblackout ( Lsegment ) P candidates ψ = 0 All segments x= 0 All v ground Z) f ( v drift )dv drift dxdψ Lsegment drift no repair ( t ground Z) (41) where size, N candidates is the number of grounding candidates of a particular ship type and P wind (ψ ) is the probability of wind from direction ψ, and Z = {x,ψ,v} is the conditional vector defining the parameters on which the time to grounding is dependent. 32

33 2.4 Causation Factor The causation factor P C is the reduction factor with which the number of accident candidates has to be multiplied to get the estimated frequency of marine accidents. Causation factor quantifies the probability of failing to avoid the accident while being on an accident course. The simplest and most traditional way to estimate the value for causation factor is to use available accident statistics. However, accident data of a limited area is too scarce so data from other areas has to be used in addition. The results derived from accident statistics of different areas have to be transformed to the analysed location as navigational conditions vary according to location which causes inaccuracy. In addition, navigational conditions rarely stay similar even in an area. The amount of traffic may increase or new safety measures may be adopted. Thus, past accident data can only help in evaluating a very rough estimate of present causation factor in an area. In addition, estimating causation factor based on accident statistics does not help in understanding what actually happens in a vessel when it fails to avoid an accident (Hänninen & Kuala 2009). To improve safety, understanding what goes wrong in a vessel before an accident is crucial. Still, e.g., Pedersen (1995) suggests that P C can be estimated from accident data collected at various locations and then transformed to the analysed area. Another approach to estimating the value of causation factor is to consider the whole chain of events leading to not making the needed evasive actions. Specific technical failures of humane errors may cause an accident if they occur before or at the same time with a critical situation. Often, many small things combined cause an accident. In practice, a fault tree or a Bayesian network can be used. (Hänninen & Kuala 2009) A fault tree is a logical diagram that determines the probability of an unwanted event. Boolean logic is used to combine a series of lower-level failures and events, such as storm, navigator asleep, or technical failures. A fault tree shows which combinations of lower-level failures lead to an unwanted event, failing to avoid an accident. (Simonsen 1997, Friis-Hansen & Simonsen 2002, Modarres 2006) Bayesian networks represent graphically the relationships between uncertain quantities. An acyclic net of nodes and directed arcs is used to show the dependence between different factors. In the case of causation factor, for example, weather, speed, 33

34 and type of vessel and alertness of navigator are relevant factors. A Bayesian Network may consist of decision nodes as well. The causation factor is then be calculated from conditional probabilities of different factors. For detailed explanation of Bayesian Belief Networks, see, e. g., reference (Trucco et al. 2008). Several factors related to weather, communication, navigational characteristics of the area, crew alertness and educational level, safety culture of the companies, and ship properties influence causation factor (Hänninen 2008). Hänninen & Kuala (2009) included parameters related to navigational aids, conditions, safety culture, personnel factors, management factors, other vigilance, and technical reliability to their Bayesian network model of causation probability. It is obvious that the numerical value of causation factor cannot be defined with high accuracy as many parameter values are ust inaccurate estimates. Consequently, causation factors are generally validated by multiplying causation factor by the number of accident candidates and then comparing results to accident statistics. Causation factors used in different studies cannot be directly compared without paying attention to the geometrical collision or grounding models they have been used with. Geometrical models give different number of accident candidates which means that in order to get a correct estimate of the number of accidents, causation factor has to be evaluated ointly with the used geometrical model. This is most clearly seen when the simplest procedure is used, that is the causation factor is evaluated by dividing the past number of accident candidates by the number of actual groundings during the same time period. Thus, causation factors reflect the underlying geometrical models and cannot, e.g., be directly applied in another study with another geometrical model even if the analysis area is the same. Some of the causation factors utilized in collision analyses are presented by Ylitalo (2009) and Friis-Hansen (2008). Mazaheri (2009) presents a somewhat complete list of causation factors used in different grounding studies. In general, causation probabilities for grounding are larger than those for collision because two ships have the possibility to make evasive manoeuvres when they are approaching each other but when a ship is on a grounding course only it has the possibility to avoid grounding (Pedersen 1995). To give an example of the magnitude of different types of causation factors, default causation factors of IWRAP are presented in Table 1. They are 34

35 adapted from the work of Fuii & Mizuki (1998) and were originally defined for Japanese waters. Table 1. Default causation factors in IWRAP. (Friis Hansen 2008) Type of encounter Causation factor Head-on Overtaking Crossing 4 Bend 4 Merging 4 Grounding forget to turn 35

36 3 Research Methods and Material 3.1 Research Methods AIS data (see Chapter 3.2) was used to get the traffic image of the Gulf of Finland in Waterway segments were formed based on a density plot of ship traffic. A counting gate was chosen in the middle of each waterway segment. At the gate, the number of ship passages and the speeds, types, and dimensions of the ships were registered. Few examples of traffic compositions on waterways are presented in Chapters The data of 2008 was multiplied by 1.02 to compensate for missing days. Information about the traffic was inserted to IWRAP (presented in Chapter 4) which calculated the number of collision candidates. To have an estimate of the number of grounding candidates, the depth curves were taken from a map and inserted to IWRAP. The numbers of accident candidates were also multiplied by causation factors to be able to compare the estimated accident frequencies to statistics. In order to make an estimate about the change of collision risk level by 2015, traffic estimates made by Kuronen et al. (2008) were used. They are presented in Chapter 3.5. Specific cargo multiplier and oil multiplier were calculated for each waterway to multiply the traffic of 2008 (Chapter 5.3.1) and thus to obtain the number of collision candidates in 2015 by IWRAP calculations. 3.2 Introduction to Data Automatic Identification System (AIS) enables ships and Vessel Traffic Services (VTS) to automatically exchange information on Very High Frequency channels. Vessel Traffic Services observe maritime traffic and remind the crew of the vessel if the vessel does not navigate in compliance with regulations. Ships are also informed, e.g., about weather conditions in the area. AIS enables ships to have automatically information about other ships that are navigating in the same area. AIS data includes static, dynamic, and voyage-related information. AIS improves marine safety as the crews have more information about other ships courses, speeds, positions and dimensions. In addition, the system enables the transmission of short safety-related messages. (IMO 2002, FMA 2009) 36

37 By the end of 2004, AIS had to be installed to all passenger ships, all ships of at least 300 gross tonnage engaged to international voyages, and all cargo ships of 500 gross tonnage and upwards even if not navigating internationally. However, some exceptions remain. Certain vessel types, for example, warships and naval auxiliaries, do not have to carry AIS. However, AIS data gives a good overall image of the traffic. (IMO 2009) AIS data is stored which enables the analysis of maritime traffic afterwards. By reproducing ship tracks it is possible to, e.g., examine how close to each other ships have navigated. In this thesis, AIS data is used to get information about ship traffic in the Gulf of Finland. Data is used to form traffic distributions across the studied waterways and to the get information about ship dimensions, speed, and courses. Unfortunately, AIS data is partially inconsistent. AIS is often not installed properly or the data is not updated as recommended by IMO (2002). For example, information ships are transmitting may contain wrong identification numbers, dimensions, or ship type. Some fields may also be left blank. Voyage related data should be updated to every ourney but it is often out of date. In addition, severe weather conditions may also disturb data exchange via AIS. (Rambøll 2006, Harati-Mokhtari et al. 2007, SSPA 2009, Lloyd s MIU 2009) The inconsistency of AIS data is increased in the storing process. The amount of data is excessive which requires a lot from the storing system. The system may be occasionally down partially or totally for some periods of time. AIS messages of those periods are lost. One point about making traffic distributions across waterways based on AIS data is that also made evasive manoeuvres are stored in the data. Ships would have possibly navigated according to slightly different distributions if they never made any evasive manoeuvres. Thus, an analysis made with AIS data does not actually give correct number of collision candidates. However, it is reasonable to assume that the error due to made evasive manoeuvres does not distort the results significantly. The movements of most small ships are not included in AIS data because they rarely carry AIS transmitters. However, they raise the risks of collision and grounding of large ships as well because they diminish the space which is available to avoid other ships and shoals in shallow waterways. 37

38 Even though the data used for the analysis is partially inconsistent, AIS enables marine accident modelling in more advanced way than what was possible before the introduction of AIS. For example, traffic distributions across waterways may be formed with the actual traffic of a year. To summarize, AIS data is valuable for marine accident frequency analysis but its disadvantages have to be noticed when interpreting the results and drawing conclusions. 3.3 Analysed Areas Area of Collision Frequency Analysis For collision frequency analysis of years 2008 and 2015, most of the Gulf of Finland was chosen as the analysis area (see Figure 13). Less used waterways and areas near ports were excluded. Few examples of traffic on the waterways are presented in Chapters 3.4.5, 3.4.6, and Figure 13. Analysed waterways of collision frequency study in 2008 and 2015 modelled in IWRAP. The numbers refer to traffic data examples of Chapters 3.4.5, 3.4.6, and A wider area was included in the monthly collision frequency analysis than in the yearly analysis for practical reasons as is seen by comparing Figure 13 and Figure 14. More waterways close to ports of Kotka and Sköldvik were taken into account. In addition, more waterways were included near the entrance to the Gulf of Finland. However, waterways to Primorsk and Ust-Luga were excluded from the monthly analysis although they were included in the yearly analysis. 38

39 Figure 14. Analysed waterways of monthly collision frequency study modelled in IWRAP Area of Grounding Frequency Analysis The area west from Kotka was chosen for the grounding frequency analysis of The study was made for two waterways arriving to Kotka from west between islands as Figure 15 shows. The waterways and the locations of 10 m, 6 m, 3 m, and 0 m depth curves are presented in Figure 16 more in detail. Grounds and depth curves were modelled in Google Earth from a map as suggested in the user manual of IWRAP. A map made by Finnish Maritime Administration was used. However, all depth curves of 3 m and 6 m were not included in the map. The traffic distributions across each waterway are also presented in Figure 16. It is seen that traffic to opposite directions use the same part of waterway. Waterways in the area are mostly narrow between islands and shallows. Figure 15. Area of grounding frequency analysis. 39

40 Figure 16. Waterway segments, grounds, and depth curves (3 m, 6 m, and 10 m) that were taken into account in the grounding frequency analysis. 3.4 Ship Traffic in the Gulf of Finland in Traffic Image AIS data of the year 2008 is used for the purpose of this study. A visualisation of ship traffic in the Gulf of Finland in January 2008 is presented in Figure 17. Ship tracks seem dotted as AIS messages from each ship were stored only once in 10 minutes in the database which was used to produce the figure. Ship traffic in December 2008 is presented in Figure 18. The main change in traffic flows during 2008 was caused by the opening of Vuosaari port in Helsinki. The induced change of traffic flows between Helsinki and Tallinn is clearly visible when comparing Figure 17 and Figure 18. However, the change is not taken into consideration in the analysis because the effect of change is not yet clearly visible even in November Most of the ship traffic in the Gulf of Finland enters the gulf or leaves it during a voyage. For instance, 50 % of petroleum cargoes transported in the Gulf of Finland in 2007 were loaded in the largest oil port of the gulf, Primorsk (Kuronen et al. 2008). Still, some ships operate only inside the gulf, especially passenger ships and high 40

41 speed crafts between Helsinki and Tallinn. Passenger vessels are moving mostly in western parts of the gulf. Figure 17. Traffic in the Gulf of Finland during the first three days of January Figure 18. Traffic in the Gulf of Finland during the first three days of December Especially in summer, many pleasure boats cross the Gulf of Finland between Helsinki and Tallinn (Hänninen et al. 2002). Some pleasure boats navigate in other parts of the gulf as well. However, the pleasure boats and other small vessels were excluded in the presented study Ice Season The ice season was exceptional in the Gulf of Finland as it was the mildest since 1720 when gathering of ice statistics was started. Ice breaking was not needed at all. The maximum ice cover of ice season , of about km 2, is presented in Figure 19. During an average winter, the whole Gulf of Finland is 41

42 covered with ice when it reaches its maximum extent. The maximum ice cover of ice season is presented in Figure 20. That ice season was categorized as average ice season by Baltic Sea Portal (2003) that is maintained by the Finnish Environment Institute, the Finnish Meteorological Institute and the Ministry of Environment in Finland. Differences between Figure 19 and Figure 20 are extensive in the Gulf of Finland. Thus, the AIS data of the year 2008 does not give information about traffic flows navigating in ice and does not help in estimating how winter affects traffic and accident probability. Thus, winter time 2008 is handled in this thesis as if it was prolonged autumn. (Baltic Sea Portal 2008) Figure 19. Maximum ice cover of the ice season 2007/2008, on 24 th of March (Baltic Sea Portal 2008) Figure 20. Maximum ice cover of the ice season 2002/2003, on 5 th of March (Baltic Sea Portal 2003) Ship Traffic Arriving to the Gulf of Finland and Leaving It Ship movements to the Gulf of Finland and away from it were counted from AIS data at the counting gate presented in Figure 21. The total number of ships registered at the gate was The monthly numbers of ships passing that gate to both directions in 2008 are shown in Table 2. Cargo vessel is the most frequent ship type amounting to 60 % of the traffic. The number of tankers and passenger vessels is similar as 18 % of ships were tankers and 15 % were passenger vessels. Less than 1 % of ships were high 42

43 speed crafts (HSC) and 7 % of ships were other ships. The group other includes tugs, pilot vessels, sailing vessels, fishing ships, yachts etc. Figure 21. The gate along the meridian 23.4 E where the number of ship movements was counted. Table 2. The monthly number of ships arriving to the Gulf of Finland (eastward traffic) and leaving it (westward traffic) in 2008 according to AIS data High speed Passenger Cargo Other Tankers crafts ships vessels ships Total January February March April May June July August September October November December Total The dependency of traffic volume on season is illustrated in Figure 22. Traffic is heavier in summer than in winter; the traffic is the heaviest in June, July, and August but December, January, and February are the months with the least traffic. The number of ship passages in February was 77 % of the number of passages in July. The winter diminishes the traffic of all ship types. In the year 2008 the economical worldwide recession started which affected marine traffic as well. After many consecutive years of traffic growth in the Gulf of Finland, 43

44 the number of ships movements at the entrance to the gulf decreased by 6.0 % from 2007 to 2008 (Table 3). However, the change was not similar in the whole gulf The number of ship movements Other Tanker Cargo Passenger HSC Month Figure 22. The number of ship movements at the entrance to the Gulf of Finland per month in Table 3. Change of ship movements by ship type at the entrance to the Gulf of Finland and at the east side of Gogland from 2007 to Passenger Cargo Tankers All ships ships vessels At the entrance to the Gulf of Finland -5.0 % -5.6 % -2.6 % -6.0 % At east side of Gogland +0.5 % -4.2 % -1.5 % -5.0 % Ship Traffic between Helsinki and Tallinn Ship traffic between Helsinki and Tallinn is busy. In 2008, ship movements to south or north were recorded on that waterway as presented in Table % of the vessels were passenger ships, 29 % were high speed crafts, 15 % were cargo ships, 4 % were tankers, and the rest were vessels of other ship types. The traffic is the heaviest in July (Figure 23). The month with the least traffic was February when traffic volume was only 37 % of the volume of July. Thus, the amount of traffic between Helsinki and Tallinn is more dependent on season than the amount of traffic at the entrance to the Gulf of Finland. Especially the high speed craft traffic between Helsinki and Tallinn is sensitive to weather conditions. Winter 2008 was unusually mild and therefore high speed crafts were able to operate more than during normal winters such as winter 2006 (Ylitalo et al. 2008). 44

45 2000 The number of ship movements Other Tanker Cargo Passenger HSC Month Figure 23. The number of ship movements per month between Helsinki and Tallinn in Table 4. The monthly number of ships navigating north or south between Helsinki and Tallinn in 2008 according to AIS data High speed Passenger Cargo Other Tankers crafts ships vessels ships Total January February March April May June July August September October November December Total Ship Traffic at East Side of Gogland At east side of Gogland (the number 3 in Figure 13), ships are safely navigating along separate lanes of the main route to opposite directions as Traffic Separation Scheme (TSS) requires. Histograms of traffic distribution across the waterway are presented in Figure 24. For the purpose of traffic modelling, these discrete values were replaced by fitted continues distributions. The distributions which fitted the best were normal distributions with the following standard deviations: μ = 525 m and southwest μ northeast = 472 m. The mean distance between the ships navigating to opposite 45

46 directions was m. Normal distributions fitted to the positions of ships across the waterway are shown in Figure 25. Figure 24. Histograms of the traffic spread across the waterway at east side of Gogland; darker bars represent traffic to northeast and lighter bars traffic to southwest. Figure 25. Distributions fitted to histograms of the traffic spread across the waterway at east side of Gogland. The traffic volume of the waterway is presented in Table ships were recorded in the waterway to west or east. General cargo ship and container ship were the most common ship types in the waterway in Table 5. Ship types and volumes in the waterway at east side of Gogland in Ship type Northeastbound Southwestbound Total Crude oil tanker Oil products tanker Chemical tanker Gas tanker Container ship General cargo ship Bulk carrier Ro-Ro cargo ship Passenger ship Fast ferry Support ship Fishing ship Pleasure boat Other ship Total Ship Traffic at west side of Kotka Ships approaching Kotka from west on two waterways are considered in grounding frequency analysis. Most of the ships are navigating along northern waterway (the number 2 in Figure 13) which is shallower than the southern one. Approximately 46

47 6 500 ships were navigating along the northern waterway to either direction in The waterway is shallow which causes ships to both directions to use the same part of the waterway as seen in Figure 26 and Figure 27 which are from the waterway segment (the number 2 in Figure 13) at west side of Kaunissaari. The length distribution of eastward ships on the waterway is presented in Table 6. Normal distributions were used to describe the traffic to both directions. The mean distance of the ships navigating to opposite directions was only 38 m. The standard deviations were μ = 148 m and μ = 137 m. Only few ships navigated in the southern southwest northeast waterway to Kotka (see Figure 15 and Figure 16): 67 ships navigated eastwards and 80 ships southwards along it in Approximately of the ships were continuing eastwards south of Kotka in the analysis area and a similar number of ships arrived from east to the analysed waterways. Figure 26. Histograms of traffic spread across the northern waterway towards Kotka (see Figure 15 and Figure 16); darker bars represent traffic to northeast and lighter bars traffic to southwest. Figure 27. Distributions fitted to histograms of traffic spread across the northern waterway towards Kotka. Table 6. Eastward traffic towards Kotka at west side of Kaunissaari in Length of ship (m) Frequency per year

48 3.4.7 Ship Traffic to Sköldvik Traffic to Sköldvik (the number 1 in Figure 13) is presented here as an example of a waterway where traffic is not normally distributed as seen from Figure 28. The used fitted distributions are illustrated in Figure 29. A combination of normal distribution (weight 0.75) and uniform distribution (weight 0.25) was used ships navigated along the waterway in Figure 28. Histograms of traffic spread across the waterway to Sköldvik in 2008; darker bars represent traffic to northeast and lighter bars traffic to southwest. Figure 29. Distributions fitted to histograms of traffic spread across the waterway to Sköldvik. 3.5 Ship Traffic in the Gulf of Finland in 2015 Three alternative traffic growth scenarios are foreseen for the Gulf of Finland: the slow growth, average growth, and strong growth scenarios (Kuronen et al. 2008). They are presented in Table 7. Only cargo and oil transports are included in the scenarios. The amount of passenger traffic is important especially between Helsinki and Tallinn but it is not supposed to increase as much as cargo and oil transportations. Thus, it is assumed that passenger traffic would not change. Table 7. Transport growth scenarios. (Kuronen et al. 2008) Scenario Growth of cargo tonnes Growth of petroleum products transport Probability of the scenario Slow growth 23 % 7 % 35 % Average growth 64 % 50 % 50 % Strong growth 93 % 78 % 15 % 48

49 The average growth scenario is the most probable scenario with the probability of 50 %. Thus, the average growth scenario is used when future accident frequency is estimated later in this thesis. It is important to bear in mind that traffic growth according to the mentioned scenario is ust one possibility of the future outcome. Thus, all calculations based on that scenario represent one, although relatively probable, change of marine traffic risk in the future. While predicting future traffic, one problem is that locations of traffic flows may change. In 2008, one maor traffic flow change was noticed due to cargo harbour operations moving to new Vuosaari port in Helsinki starting on the 24 th of November 2008 (Port of Helsinki 2008). This change is seen only in traffic flows of November and of December Thus the change is not possible to take into account in the estimate for Other possible reasons for traffic flow changes include new traffic regulations, change in aids to navigation etc. For this thesis, ship traffic of 2015 was estimated by multiplying the traffic of 2008 according to growth estimates made for SAFGOF proect. Cargo volume estimates per port were taken from Kuronen et al. (2008). More specific oil volume estimates were taken from Kuronen (2009). Unfortunately, Kuronen et al. (2008) present cargo volumes of 2007 and estimates of 2015 whereas the AIS data available for this thesis was the data of the year The number of ships moving at the entrance to the gulf decreased by 6.0 % from 2007 to 2008 (Table 3). The number of tankers decreased the least, 2.6 %. However, the decrease was not steady at the whole Gulf of Finland as is seen from Table 3. The change was smaller on the main route at east side of Gogland and the number of passenger vessels even grew on that particular waterway. Overall, traffic volumes diminished in the Gulf of Finland from 2007 to 2008 which should be taken into account when examining the results of collision frequency estimation for the year However, the magnitude of the change is only approximately 5 % and future estimates are never precise. So the utilization of the data of the year 2008 is not the greatest source of inaccuracy. Though, it is good not to forget it when evaluating the results of the analysis. 49

50 4 IWRAP Mk2 4.1 Introduction IWRAP is software for evaluating grounding and collision probabilities. It is also known as BaSSy tool or GRISK. IWRAP is abbreviation for IALA Waterway Risk Assessment Program. The use of IWRAP as a part of risk analysis is recommended by IALA (2009a). Versions GRISK 2.1.1, IWRAP Mk2 v2.1.2, and IWRAP Mk2 v2.2.0 of the program were used in the analysis. All information about waterway locations, ship traffic and grounds has to be inserted by the user. For the analysis of an area where seasonal traffic variation is important, as in the Gulf of Finland, AIS data of one year is needed. IWRAP gives results as the frequency of head-on, overtaking, merging, crossing, and bend collisions. The relative risk of each waterway and waypoint is marked on the map. It is also possible to contemplate collision frequencies at certain waterway or waypoint. In addition, overall collision frequencies or frequencies at certain location or of certain collision type are presented by ship type. If grounds and depth curves have been defined, IWRAP also gives the frequency of powered and drifting groundings. Grounding locations are presented by a picture where relative risk is presented using colour scale from yellow to red. Red highlights the areas of the highest risk. The development of IWRAP has not yet ended. Some new features are suggested in Appendix A. 4.2 Discussion on Collision Frequency Models The collision frequency models of IWRAP (Friis Hansen 2008) were presented in Chapter The idea behind the models is the same as in models of Pedersen (1995) and COWI (2008). However, the model of COWI is a little bit simplified version of Pedersen s model. The idea of the model of Fowler and Sørgård (2000) is somewhat similar but no practical means to calculate the meeting frequency is presented in their paper. In addition, they define a critical situation as two ships crossing each other at shorter distance than 0.5 nm although all those situations would 50

51 not lead to collision even without any evasive manoeuvres. Their work represents thus the trend to think that ships have a certain safety domain around them (Friis-Hansen et al. 2004, Pietrzykowski & Uriasz 2009). The navigator wants to keep that domain, socalled ship domain, free of other ships and obects. This approach takes into account that it is not safe to pass another ship at a very small distance. Ship domain approach to geometrical collision possibility leads to smaller causation probabilities than, for example, Pedersen s model. In the models of Pedersen, IWRAP, and COWI, only the number of ships that would actually collide without evasive manoeuvres is considered. However, the actual manoeuvring capabilities of ships do not affect the calculated geometrical collision diameter. Montewka et al. (2009) have presented a way of calculating geometrical collision diameter by simulating ship manoeuvres. In their paper, Minimum Distance To Collision (MDTC) is defined as the minimum distance between the centres of two navigating vessels when it is still possible for the vessels to perform succeeding collision avoidance manoeuvres. That kind of approach is particularly needed when collision risk is estimated at real time. The physical characteristics of the traffic affect the collision frequency in IWRAP, as well as in the models of Pedersen, Fowler and Sørgård, and COWI. The location of waterways and angles between them affect the results as well as the number of passages and the traffic distribution across the waterway. However, in IWRAP it is only possible to use continuous distribution to describe the traffic spread across the waterway; the utilization of histograms of actual traffic is not supported. Ship dimensions do not affect the results got by the model of Fowler and Sørgård but the models of Pedersen, COWI and IWRAP take them into account. It is thus possible to consider the significant variety of ship dimensions, speed and types opposed Macduff s model that assumes all ships to be similar. However, all the presented models lack the influence of time in the sense that the movements of ships are modelled as a Poisson process. Actual traffic volume varies according to time of day and season and thus the inter-arrival times are not exponentially distributed. In addition, when traffic distributions across the waterway and traffic volumes are used to describe traffic, ships can even theoretically collide with themselves. The bend collision model of IWRAP assumes that 1 % of ships arriving to a bend of a waterway do not turn as they should and thus may become collision candidates (Friis- Hansen, 2008). The author has not find reasoning why it could be supposed that 1 % 51

52 of the ships would not turn when they should at a bend. The probability of missing a turn is much larger than causation probability although its reasons appear to be similar; human or technical error. However, the percentage of ships missing the turn is important because the number of bend collision candidates seem to be linearly dependent on the percentage of ships omitting to turn. The model implemented in IWRAP is also capable of giving an estimate how changes in regulations affect the risk level. If an expert is able to give a forecast how a change in regulations could affect, new calculations can be made with IWRAP to estimate how the change would influence collision frequency. An example could be that a change in regulations is expected to change traffic spread across a certain waterway. The effect of such a change on collision frequency is easy to estimate with IWRAP. Navigation conditions, e.g., the weather, can be taken into consideration only in causation factor. IWRAP offers a default value for the causation factor in each collision and grounding scenario. Different causation factors can be defined for different waterways, ship types, and sizes. The program itself calculates only the number of collision candidates. The evaluation of causation factor is left to the user. However, Friis-Hansen (2008) describes how to define a causation probability adusted to study area. Accident statistics do not directly affect the results obtained from IWRAP. Geometrical collision frequency is evaluated without any influence of statistics. The possible effect of statistics is included in causation factor. Still, causation factor may be estimated without knowledge about accident statistics. The value of causation factor is usually validated by accident statistics. However, using accident statistics as a part of analysis has some advantages. For example, if many accidents have taken place at a certain area, some common factor may have affected to most of the accidents. Pedersen s approach to modelling the number of collision candidates causes some issues, e.g., if the vessel speed increases, the number of crossing collision candidates diminishes because ship tracks intersect for shorter time period (Ylitalo 2009). In real world, however, ships have also less time to detect each other and to make evasive manoeuvres if they are on a collision course. These effects should be taken into account in causation factor. In a way, causation factor includes all problematic issues. However, even in recent times, the modelling of causation factor has not been at the 52

53 focus when using collision frequency models. In contrary, causation factor is still often taken from other studies or derived from accident statistics without considering local navigational characteristics or technical changes as the introduction of AIS. The parameter to which the geometrical collision frequency model used in IWRAP is the most sensitive is the number of ships (Ylitalo 2009, see also Chapter 5.1.3). However, the number of ships and the other parameters can be gathered from AIS data, they do not have to be estimated. Then, the question is about the reliability of AIS data. The validity of the bend collision model is also questionable due to the assumption of 1 % of ships not to turn at a bend of the waterway. Pedersen s model is nowadays widely used (Pedersen & Zhang 1999, Otto et al. 2002, Rambøll 2006, Nyman et al. 2009). Collision frequency part of IWRAP has been validated in several areas by its developers. Different models for collision frequency analysis are available but they usually use the same approach to the problem: split the model into causation factor and the number of collision candidates. 4.3 Discussion on Grounding Frequency Models The grounding models of COWI (2008) and Pedersen (1995) have similar features as the models used by IWRAP. All they are able to analyze the probability of grounding also to islands contrary to the model of Fowler and Sørgård (2000) that considers only coastline. The idea of the scenario of omitting to turn at a bend of a waterway is similar in Pedersen (1995), Simonsen (1997), COWI (2008), and IWRAP (Friis Hansen 2008) but the calculation procedures differ. As in the case of collisions, the grounding frequency model of IWRAP takes well into account the differences of the ships navigating in the area. In the case of grounding frequency, the different draughts of different ship groups are of special importance. Different coastlines and archipelagos can be modelled and taken into account in IWRAP on the contrary to the model of Fowler and Sørgård. However, the depth curves have to be manually inserted to the program at present. IWRAP s grounding model enables the comparison of the safety of waterways. Also the effect of safety measures, such as Traffic Separation Schemes (TSS), can be estimated according to estimations how the change would affect traffic distribution across the waterway or other traffic parameters. 53

54 In addition to estimating a value for grounding causation factor, an estimate about the average length between position checks by the navigator has to be assessed when using IWRAP. Thus, human factor does not affect only causation factor but also the number of grounding candidates. The average length between position checks by the navigator is a parameter of which the exact value is difficult to estimate. However, results are sensitive to that parameter value which causes deviation to results (Ylitalo et al. 2008). According to Friis-Hansen (2008), the default value of the average length between position checks by the navigator, 180 s, is that long because the vigilance of the crew is supposed to have diminished because the ship has already omitted a turn. The question remains how accurately a parameter defining such a rare situation could be defined and how much the average value reflects the spectrum of real values. In addition, the validation of the value of the average length between position checks by the navigator and of causation factor is difficult as they have to be validated together against grounding statistics. The grounding model of IWRAP has been validated in few areas by its developers. However, it includes more parameters that are difficult to define and validate than the collision model. 4.4 IWRAP and the Gulf of Finland IWRAP provides a calculation method to estimate accident frequencies over large sea areas. However, also the special characteristics of specific waterways can be taken into consideration in the analysis. This is a good starting point for the analysis of the Gulf of Finland. Without such a program, a lot of work is needed to calculate accident frequencies by any published model. With IWRAP, a user only has to have information about marine traffic of the studied region. However, information about traffic requires a lot of AIS data and a lot of data processing. Traffic information can be inserted to the program and it calculates results in few seconds. This kind of tool is definitely needed to model accident frequency easily. In addition, forecasted accident frequency can be calculated with the forecasts of future traffic and the benefit gained by new safety measures can be estimated. The number of small vessels can also be inserted to IWRAP program but the number is supposed to be uniformly distributed in the whole analysed area during the study period which is far from the reality in the Gulf of Finland. 54

55 It is good that IWRAP has in-built average velocities for each ship type and length group. However, especially when analyzing narrow waterways between islands, it is important to gather actual speed information from AIS data as the actual average speed may be much smaller than the average speed of the same ship group in open sea. IWRAP has inbuilt values of average draughts for different ship groups. They do not seem to be suitable to be used in the analysis of the Gulf of Finland. The reason may be that the gulf is relatively shallow. The largest vessels cannot even navigate to the Baltic Sea as draught of vessels is limited in Danish straits. Draught is limited in many ports in the Gulf of Finland as well. For example, for tankers of m, IWRAP uses the default value of 17.2 m. Three tankers of that length group sailed to Sköldvik in 2008 even though the depth of the waterway to Sköldvik is only 15.3 m. Thus, the actual draughts of vessels navigating in the studied area have to be inserted in IWRAP when calculating grounding frequency. However, gathering actual draughts of ships is not as easy as it could be. Current draught of vessel should be included in voyage related AIS messages but the field is not updated as often as it should be. Thus, also using draught information of AIS messages is a source of uncertainty. No models for collision frequency in ice conditions exist yet. The modelling approach used in IWRAP cannot be applied directly in ice conditions. The location of ice channels does not remain constant as currents and winds move ice. Thus, neither remain traffic distributions of ships across waterways constant because the locations and widths of ice channels may vary significantly. So the standard deviation of the position of ships across a waterway defined based on AIS data of the waterway does not tell about the actual conditions in which the ships are meeting each other in the channel. One way to reduce the influence of ice movements to traffic distribution is to analyse shorter time periods instead of a period of one month. Thus ice fields have less time to move. The problem here is that during, for example, a week, only a small number of ships navigate on a waterway and thus defined distributions are less accurate. IWRAP does not have any inbuilt features to make sensitivity analysis. Though, sensitivity analysis of the results can be made manually by adusting chosen values. 55

56 The aim of IWRAP is to give an analytical result of accident frequency. It is a tool that can be used alongside with expert estimates to evaluate maritime accident risks. IWRAP does not help in modelling collision frequency with ice-breakers but it would be too much asked for a similar program especially because no models for predicting them even exist at the time when this thesis is written. In 2009, IWRAP is the best available tool to analyse and predict accident frequencies and their change in the future in the Gulf of Finland. IWRAP is relatively practical to use. Still, the person using the program needs to be familiar with accident frequency modelling and especially with the models IWRAP uses to be able to make rational choices, such as choosing values for causation factor and the parameter average length between position checks by the navigator, and to analyse the validity of the results. This need is also recognized by IALA (2009a). The process of frequency analysis with IWRAP has to be made with care and commitment and results have to be analysed by experts to estimate their reliability. 56

57 5 Results 5.1 Collision Candidates in the Gulf of Finland in Yearly Collision Candidates The numbers of different types of collision candidates obtained using IWRAP are presented in Table 8. The overall number of collision candidates during a year is The most frequent collision candidate type is bend collision candidate (40 %). The percentage is high given that the assumption of 1 % of ships omitting a turn is not well ustified (see Chapter 4.2). The share of merging collision candidates is the smallest (2 %) which is a bit surprising as there are many merging locations in the Gulf of Finland. Table 8. The number of ship-ship collision candidates in the Gulf of Finland in Ship-ship collision candidate type Collision candidates per year Percentage of total collision candidates Bend % Overtaking % Head-on % Crossing 73 4 % Merging 44 2 % Total 1890 When examining the number of different types of collision candidates, it is good to pay attention to the differences of typical causation factors used with different types of collision candidates. For instance, causation factor used with head-on collision candidates is generally the smallest which decreases the relative frequency of head-on collisions. Although overtaking and head-on collision candidates constitute together 53 % of all collision candidates, their share of the collision frequency is smaller. The relative number of collision candidates of each waterway and waypoint is illustrated in Figure 30. The number of collision candidates is the highest for the waterway at east side of Gogland (number 3 in Figure 30) and then for the waterways to St. Petersburg (6 in Figure 30) and to Kotka (1 in Figure 30). The waypoints with the most collision candidates are two waypoints north from Seiskari (4 and 5 in Figure 30) and one at west side of Gogland (2 in Figure 30). In addition, the number of 57

58 collision candidates is moderately elevated along the main route through the Gulf of Finland. Figure 30. The number of collision candidates of waterways and bend, merging and crossing locations in Scale from yellow to red is used: red signifies the highest number of collision candidates in the analysed area. The numbers of collision candidates of the three waterways having the most collision candidates are presented in Table 9. On the way to Kotka, most of the collision candidates are head-on collision candidates whereas on the other two waterways the maor type of collision candidate is overtaking collision candidate. The number of collision candidates in the waterway at east side of Gogland is 8.4 % of parallel collision candidates in the whole analysed area. Table 9. The yearly number of collision candidates for the waterways with the most collision candidates. Waterway location Number in Share of parallel Overtaking Head-on Total Figure 30 collision candidates To Kotka % East side of Gogland % To St. Petersburg % The waypoints with the highest number of collision candidates were identified and marked in Figure 30. The number of collision candidates of each waypoint is presented in Table 10. At all three locations, the most important collision candidate type is bend collision candidate. The number of collision candidates at the waypoint, at which the waterway from Primorsk merges to the main route (number 5 in Figure 30), is 18 % of waypoint collision candidates of the whole analysis area in the Gulf of 58

59 Finland. The waypoints with the most collision candidates contribute about twice as much to the overall number of collision candidates as the waterways with the most candidates. Table 10. The yearly number of collision candidates of the three most risky bending, merging and crossing locations. Number in Figure 30 Bend Merging Crossing Total Share of waypoint collision candidates % % % The most probable type of striking ship in a potential ship-ship collision situation is cargo ship (70 %) as seen from Figure 31. The most probable type of struck ship is also cargo ship (67 %). The probability of the striking ship to be an oil tanker is 13 % whereas the probability for the struck ship is 15 %. However, causation factor has not been taken into account in these numbers and possible reduction factors for certain ship types could change the percentages. 80 % 70 % 60 % Percent 50 % 40 % 30 % Struck ship Striking ship 20 % 10 % 0 % Tanker Cargo vessel Passenger ship High speed craft Other vessel Figure 31. The distribution of struck and striking ship types in potential collision situations. Overall, the main route from the entrance to the Gulf of Finland to St. Petersburg through the whole gulf seems to be the most risky waterway. That route gets narrower towards east which can be easily seen from Figure 17 and the number of collision candidates is rising. Ships navigate closer to each other and thus the route segments are more dangerous in the eastern part of the gulf than in the western part even though the volume of traffic is greater in western part. 59

60 The causation factors Hänninen and Ylitalo (2010) have defined for the Gulf of Finland for the year 2008 are shown in Table 11. Ship types that navigated in the Gulf of Finland in 2008 were taken into consideration in the definition process. When the yearly numbers of different types of collision candidates are multiplied by the causation factors, the estimate of the yearly collision frequency is 0.26 collisions per year. It would mean a collision per 3.8 years in average. The share of bend collisions is as high as 74 % of estimated yearly collision frequency. Table 11. The results of the yearly collision frequency analysis of Used causation factor (Hänninen & Ylitalo 2010) Bend Overtaking Crossing Merging Head-on The number of collision candidates Estimated yearly collision frequency % % % % % Total Percentage of collision frequency Monthly Collision Candidates The estimated monthly numbers of different types of collision candidates in the Gulf of Finland in 2008 are presented in Table 12. Traffic is the heaviest in summer (see Figure 22 and Figure 23) so also the number of collision candidates is the largest in May and July. The dependence of the number of collision candidates on the number of ship movements at the entrance to the Gulf of Finland is strong as can be seen from Figure 32. However, it should be noticed that ice conditions are not included into the modelling. Table 12. The estimated monthly numbers of different types of collision candidates in the Gulf of Finland in January March May July September November Bend Overtaking Head-on Crossing Merging Total

61 Collision candidates by month Ships at the entrance to the Gulf of Finland January March May July September November Figure 32. The dependence of the number of collisions on traffic volume. The most important collision candidate types per month are illustrated in Figure 33. The share of each collision type of total collision candidates does not change significantly from month to month. For example, the share of head-on collision candidates varies between 20 % and 25 %. 300 Number of collision candidates Bend Merging Crossing Head-on Overtaking 0 January March May July September November Figure 33. The monthly numbers of collision candidates in The area under analysis for the monthly collision candidates was larger than for the analysis of the whole years 2008 and 2015 (Figure 13 and Figure 14) and therefore two additional risky waypoints are visible at the western end of the analysis area (see e.g. Figure 34). Those two waypoints are the waypoints with the highest number of collision candidates through the whole year. However, the collision candidates at the 61

62 locations are all bend collision candidates. The importance of those two waypoints is also high: % of the total number of collision candidates. The relative number of collision candidates on waterways and at waypoints are presented in Figure 34-Figure 39. In January (Figure 34), the waterways with the most collision candidates are located between ports of Kirkkonummi and Tallinn, on the way to Kotka, and east side of Gogland. Figure 34. The number of collision candidates in waterways and bend, merging and crossing locations in January Scale from yellow to red is used: red signifies the highest number of collision candidates in the analysed area. In March (Figure 35), the waterways with the highest number of collision candidates are on the way to Kotka, east side of Gogland, and to St. Petersburg. Figure 35. The number of collision candidates in waterways and bend, merging and crossing locations in March Scale from yellow to red is used: red signifies the highest number of collision candidates in the analysed area. 62

63 In May (Figure 36), there are the highest number of collision candidates in the waterway at east side of Gogland, to St. Petersburg, and between St. Petersburg and Primorsk. Figure 36. The number of collision candidates in waterways and bend, merging and crossing locations in May Scale from yellow to red is used: red signifies the highest number of collision candidates in the analysed area. In July (Figure 37), the waterways with the highest number of collision candidates are the same than in May. However, 69 % of the ship movements in the waterway between St. Petersburg and Primorsk are made by other ship types than tankers, cargo vessels, passenger ships and high speed crafts. July is the month of busiest traffic between Helsinki and Tallinn (see Figure 23). Despite that, the crossing area between the cities does not seem particularly risky. Figure 37. The number of collision candidates in waterways and bend, merging and crossing locations in July Scale from yellow to red is used: red signifies the highest number of collision candidates in the analysed area. 63

64 In September, the waterways with the highest number of collision candidates are at east side of Gogland and to St. Petersburg. Figure 38. The number of collision candidates in waterways and bend, merging and crossing locations in September Scale from yellow to red is used: red signifies the highest number of collision candidates in the analysed area. In November (Figure 39), the number of collision candidates is the highest between the ports of Sköldvik and Tallinn and at east side of Gogland. Figure 39. The number of collision candidates in waterways and bend, merging and crossing locations in November Scale from yellow to red is used: red signifies the highest number of collision candidates in the analysed area. Overall, the contribution of the main route through the gulf is significant during all seasons. The importance of its parts depends on the analysed month. Partly the reason is that traffic distributions are varying from month to month ust because each monthly set of ship movements is one realisation of a stochastic process. When making monthly analysis, each set of ship movements is smaller than for yearly 64

65 analysis and thus smaller phenomena affect more the resulting traffic distributions across the waterways than on yearly level Sensitivity Analysis The aim of sensitivity analysis is to study the relative influence of the inputs and their uncertainty to the uncertainty in results. The simplest form of sensitivity analysis, one-at-a-time sampling, is conducted in this thesis. It means that only one parameter is changed at a time and the effect on the results is studied. (Saltelli 2002, Helton et al. 2006, Saltelli et al. 2008) Merrick & van Dorp (2006) have criticized the lack of sensitivity analysis in marine risk assessments. IWRAP does not include inbuilt tools for estimating the uncertainty of the results. However, several features such as traffic volume adustment can be used for sensitivity analysis. The effects of increasing traffic volume and varying traffic distribution are studied below. Ylitalo et al. (2008) have presented sensitivity analysis of Pedersen s model by increasing also ship length, ship width, ship speed, and meeting angle. The most important sources of uncertainty in traffic volume are technical errors in AIS data storing process. It is not probable that real traffic volume would have been smaller than used in the analysis but it is likely to have been greater. If the traffic volume is multiplied by 1.1, the number of collision candidates increases by 20 % to The number of different types of collision candidates increases similarly: between 20 % and 21 %. The relative importance of waterways and waypoints does not change significantly when all traffic is multiplied by the same factor. On the contrary, the uncertainty of traffic distributions across waterways is often significant. The estimation of distribution is based on a sample that is not sufficient in all cases. In addition, distributions are always fitted distributions and their quality may vary. The impact of varying standard deviation is studied in the waterway at east side of Gogland (number 3 in Figure 30). Normal distributions were fitted to the traffic as explained in Chapter The effects on the results of halving or doubling the standard deviations of traffic across the waterway are presented in Table 13. Halving the standard deviations increases the total number of collision candidates by 4 %. All additional collision candidates are overtaking collision candidates of which the number increases by 13 %. Doubling the standard deviations decreases the number of 65

66 collision candidates by 2 %: the number of overtaking collision candidates is diminished by 7 % but the number of head-on collision candidates increases by 2 %. Traffic distributions across waterways do not affect the number of crossing, merging, or bend collision candidates. The example location for the study of varying traffic distribution across the waterway was the waterway with the most collision candidates. Halving or doubling the standard deviation is a rather massive act and it is likely that the distributions can be fitted with more precision. However, only one standard deviation was modified in the example but it had a clear impact on the results. Table 13. The change of the number of collision candidates when varying the standard deviation of traffic distribution across the waterway at east side of Gogland. Collision candidate type The base case Halved standard deviation Doubled standard deviation Overtaking Head-on Crossing Merging Bend Total According to calculations made above with varying few variables, the author can conclude that the model is rather sensitive to input values. This has to be remembered when drawing conclusions based on this study Collision Statistics When collision statistics of the Gulf of Finland in and (Kuala et al. 2009) were taken a closer look at (Hänninen & Ylitalo 2010), only two collisions had taken place in the area of the yearly analysis without tough ice conditions mentioned as a cause of the accident. Although, the occurrence of collisions is a stochastic process and the number of occurred collisions is ust one realization of that process. Therefore it only gives a hint about the actual risk level and may not be used as full information about the risk level of the area. To have more information about the actual traffic, an analysis about encounters in the Gulf of Finland (Berglund & Huttunen 2008) was examined. The analysis was made based on AIS data and an encounter was defined as two ships passing each other at a 66

67 shorter distance than 0.3 nm. The analysis was made only for June, July, and August in 2006, 2007, and The analysed area was open sea between Helsinki and Tallinn so that about 90 nm of the east-west traffic was considered. The results of the analysis for the summer months 2008 are presented in Table 14 with the results obtained from IWRAP for July Table 14. The monthly number of encounters in 2008 (Berglund and Huttunen 2008) and the result of IWRAP made for the same area with AIS data of July Encounters per month (Berglund & Huttunen 2008) The monthly number of collision candidates in July Head-on Crossing, merging, and bend Overtaking Total Discussion The result, 0.26 annual collisions, is well in line with the yearly collision frequency of 0.25 derived from accident statistics by Hänninen & Ylitalo (2010). In practice, no better indicator about the actual risk level exists than the collision statistics. Thus, results are compared to collision statistics. It is still important to remember that navigational conditions change every year which affects the yearly number of collisions in the extent that even a perfect model would not be able to predict accurately the number of accidents. The results differ significantly from the number of actual encounters (Table 14) collected from AIS data. However, the number of encounters may include ship pairs that have not made any evasive manoeuvres and still have not collided. In addition, the number of encounters does not include all ships that would have collided without evasive manoeuvres because with successful avoiding manoeuvres ships may have passed each other at longer distance than 0.3 nm. The analysis of encounters (Berglund and Huttunen 2008) was made with summer traffic which is heavier than winter traffic. Still, the results of the analysis about the number of encounters provoke the question whether IWRAP is capable of making reliable estimates about collision frequency. As the sensitivity analysis, also monthly numbers showed well the dependence of the number of collision candidates on traffic volume. In addition, the analysis showed that 67

68 collision risk is different during different months and therefore it is important to study and model monthly collision frequency in the Gulf of Finland. Monthly differences of traffic volume should be taken into consideration in future collision frequency estimates. Surprisingly, the relative number of collision candidates of waypoints between Helsinki and Tallinn is not elevated. Though, the model considers the arrival of ships to be a Poisson process. Especially between Helsinki and Tallinn, that assumption is not true as passenger traffic operates mainly at day time. In addition, the number of passenger ships and high speed crafts at the area is smaller in winter than in summer (see Table 4). If the most important consequences are considered to be lost human lives, the risk is higher between Helsinki and Tallinn than shown by this analysis as a large number of passenger vessels and high speed crafts carrying a lot of people move in that area. On contrary, only few passenger vessels navigate in the eastern Gulf of Finland. The technical reliability of passenger vessels is usually better and the number of officers on the bridge greater than in other vessels. Many passenger ships also navigate back and forth along the same route which makes the crew very familiar to navigational characteristics of the area. Thus, smaller causation probability is often used for passenger ships and high speed crafts than for other vessels. In this thesis, the same causation factors were used with all ship types. The studied area in the Gulf of Finland does not cover the whole gulf which means that the actual number of collision candidates in the gulf is larger than the number got from this analysis. For example, many collisions occur at restricted areas close to ports that have not been included in the analysis. However, the speed of ships is the highest at open sea which means that if a collision takes place in open sea, its consequences are presumably more severe than, for example, consequences of collisions in port areas. February and March are the riskiest months for ship-ship collisions (Kuala et al. 2009). Therefore, it is evident that ice conditions have an important impact to collision probability even though they are excluded from this analysis. The crossing area between Helsinki and Tallinn has been kept as the most risky area in the Gulf of Finland when considering collisions. This analysis ends up to somewhat different results: only 4 % of collision candidates in the whole analysis area are 68

69 crossing collision candidates. Collisions seem to take place the most often on the main route from the entrance to the Gulf of Finland to its most eastern parts. Interestingly, also collisions statistics support this finding when collisions in vicinity of ports are not considered (Hänninen & Ylitalo 2010). 5.2 Grounding Candidates in the Gulf of Finland in Results In this thesis, grounding frequency is estimated only for the waterways at the west side of Kotka. This study is made in order to evaluate how well IWRAP suits the needs of modelling grounding probability in the Gulf of Finland. A specific parameter value for mean time between checks was not defined for the Gulf of Finland in this thesis. Therefore, the default value of IWRAP, 180 s, was used. However, the effect of changing the value of mean time between checks is studied in Chapter The estimated powered and drifting grounding frequencies for 2008 are presented in Table 15. The estimated number of grounding candidates was The default causation factor for groundings adopted in IWRAP is No work has been published about adusting grounding causation factor for the Gulf of Finland, so the default value is used to get an estimate of grounding frequency. A powered grounding frequency of 0.26 per year was obtained. Table 15. The estimated yearly powered and drifting grounding frequencies in the analysed area in Powered grounding frequency 0.26 Drifting grounding frequency 0.05 Total grounding frequency 0.31 The drifting grounding probability per year of 0.05 was calculated. However, the default drifting grounding settings were used in IWRAP. Though, local wind and current conditions were not taken into account in the analysis: it was assumed that it is as likely to drift to all directions. The default drift speed is 1 knot (see Chapter for the impact of changing the value). The drifting grounding probability was 19 % of the total grounding probability. The total yearly grounding frequency of 0.31 was obtained. It would mean a grounding in every 3.2 years. 69

70 The number of powered grounding candidates of grounds and depth curves in the analysed area are presented in Figure 40. The number was the highest for a small island northwest from Kaunissaari. The number was higher than elsewhere also for the following locations: a small island north from Viikarinsaari and shallows west from Kaunissaari and east from Lehtinen. Apart from the shallow east from Lehtinen, there is a line light at all mentioned locations which decreases the risk of grounding. Figure 40. The number of powered grounding candidates for grounds and depth curves in Scale from yellow to red is used: red signifies the highest number of grounding candidates in the analysed area. Also the waterways are coloured according to the relative number of collision candidates in the area. Cargo ship is the most probable ship type to run aground in the analysed area. It is also the most common ship type in the area. The drifting grounding probabilities for the analysed area are illustrated in Figure 41. Mussalo port is categorized as the most probable drifting grounding location. In addition, the drifting grounding probability is higher, e.g., at the north coast of Kaunissaari than the average of the analysed area. The drifting grounding probability of practically all grounds taken into account in the analysis is non-zero. It is not surprising as blackouts are assumed to occur at any time. However, it has to be noticed that specific wind and current conditions of the area has not been taken into 70

71 consideration. They could change the relative risk of grounds and shallows and also affect the drifting grounding probability. Figure 41. The probability of drifting grounding for grounds and depth curves in Scale from yellow to red is used: red signifies the highest number of grounding candidates in the analysed area. Also the waterways are coloured according to the relative number of collision candidates in the area. The combined probability of powered and drifting grounding to grounds and depth curves in the analysed area is illustrated in Figure 42. The most risky ground is the small island northwest from Kaunissaari as in the case when only powered groundings were considered (Figure 40). Otherwise, the probability of grounding is similar for all other grounds and depth curves. 71

72 Figure 42. The probability of powered and drifting groundings for grounds and depth curves in Scale from yellow to red is used: red signifies the highest number of grounding candidates in the analysed area. Also the waterways are coloured according to the relative number of collision candidates in the area Sensitivity Analysis Traffic volume is an important factor in grounding frequency analysis. The effects of changing traffic volume on the estimated number of powered grounding candidates are presented in Table 16. If traffic volume diminishes with 10 %, the number of grounding candidates diminishes by the same percentage. The probability of a ship being a grounding candidate does not depend on the traffic volume (Chapter 2.3.5). Thus, the number of grounding candidates is linearly dependent on traffic volume. The actual traffic volume is likely to be larger than what is evaluated based on AIS data due to occasional problems in the storage process of the data. The value for the parameter mean time between checks is difficult to define precisely. However, changes in the parameter value affect significantly the number of powered grounding candidates. If the value is reduced by 30 s to 150 s, the number of candidates diminishes by 20 %. If the value is raised to 210 s, the number of candidates raises by 22 %. The use of approximately the value of 3 minutes for mean time between position check is adviced in IWRAP s manuals (Friis-Hansen 2008, 72

73 Sonne Ravn et al. 2009). However, no further information is given about when to choose different value, why the value has been chosen as the default value, or how the value has been validated. One approach is to state that by choosing 210 s instead of 150 s, the number of grounding candidates is 52 % higher but still the uncertainty caused by the selection of the parameter value may be less important than uncertainties caused by other factors. Table 16. The impact of traffic volume changes on the number of grounding candidates. Traffic volume adustment factor Powered grounding candidates Change -10 % + 10 % +20 % Table 17. The impact of the value of mean time between checks on the number of grounding candidates. Mean time between checks 150 s 180 s 210 s Powered grounding candidates Change -20 % % The chosen drift speed affects the drifting grounding probability significantly as presented in Table 18. If drift speed is doubled to 2 knots, the estimated drifting frequency increases 45 % to Table 18. The impact of the assumed drift speed on the yearly drifting grounding frequency. Drift speed 1 knot 2 knots 3 knots Drifting grounding frequency If the blackout frequency of all ships is increased to 1 blackout per year, the estimated frequency of drifting groundings increases to 0.08 per year. If the blackout frequency of all ships is assumed to be only 0.1, the estimated yearly drifting frequency is diminished to All studied changes affect significantly the results. The uncertainty of the inputs is important and causes the results to be uncertain as well because they are sensitive to small changes of the inputs. 73

74 5.2.3 Grounding Statistics According to Kuala et al. (2009), no groundings have happened in the analysed area between in and According to Helsinki Commission, two groundings have occurred in the analysis area between 1989 and 2008, namely in 1989 and Thus, the yearly grounding frequency has been One of the groundings occurred west from Kaunissaari and the other south from Örrengrund. However, 20 years is a very long reference time because ship traffic to Kotka has increased notably in 21 st century and new safety measures have been introduced to the Gulf of Finland Discussion The grounding frequency got from the analysis (0.31 groundings per year) is about three times the realized frequency (0.10 groundings per year). However, traffic volume has recently increased significantly and the occurrence of groundings is a stochastic process. Thus, the results are reasonable when compared to accident statistics. Specific values for grounding causation factor and mean time between checks for the area have not been defined and typical currents and winds have not been considered in the analysis. However, the mentioned factors affect the results significantly as shown in Chapter Therefore, the results can be only regarded as indicative. Their uncertainty is important. To have more reliable results, the above mentioned issues should be addressed. Any effects ice has on grounding frequency should also be studied. Overall, no grounds requiring special attention were identified during the analysis. Grounding risk is rather evenly distributed in the analysed area. One island was identified to have an increased powered grounding probability. However, there is already a leading light located on the island which increases the awareness of the island on ships. 5.3 Collision Candidates in the Gulf of Finland in Traffic Multipliers Separate cargo volume multiplier and oil volume multiplier were calculated for each port based on estimated growth of cargo and oil transports of each port in average 74

75 growth scenario (Table 19 and Table 20). Thus, port multipliers were used to multiply traffic in the waterways to the ports. For other waterways, multipliers were deduced as a combination of multipliers of merging waterways in relation to traffic volumes. The traffic distributions across each waterway were assumed to remain the same. In addition, the size distribution of ships was supposed not to change. The traffic of 2008 was multiplied with the multipliers to get an estimate of the traffic of The numbers of passenger ships, high speed crafts, and other ships were not changed as no similar estimates about the change of their volume were available. Percentages of traffic continuing to different waterways at waypoints were also adusted to changed traffic volumes on waterways. Table 19. Cargo volume multipliers from 2007 to Port Cargo (M tonnes) 2007 Cargo (M tonnes) 2015 Cargo volume multiplier Helsinki Sköldvik Kotka Hamina Hanko Vysotsk Primorsk St. Petersburg Ust-Luga Sillamäe Tallinn Table 20. Oil volume multipliers from 2007 to Port Oil (M tonnes) 2007 Oil (M tonnes) 2015 Oil volume multiplier Helsinki Sköldvik Kotka Hamina Hanko Vysotsk Primorsk St. Petersburg Ust-Luga Sillamäe Tallinn No oil was transported from Ust-Luga in 2007 so the number and size of tankers navigating there was obtained by assuming that the size distribution of tankers was similar to that of the tankers that navigated to St. Petersburg in The estimated number of tankers, 955 to each direction, was added to eastern waterway to Ust-Luga. 75

76 Tankers are more probably navigating along that waterway than along the western waterway because all tankers (according to AIS data only 13 in total to both directions) navigated along the eastern waterway in Moreover, all ships of which the draught was more than 5.9 m navigated along the eastern waterway. However, no accurate information was used about what size of tankers will navigate to Ust-Luga in 2015 or which waterway they will use Collision Candidates When the traffic of 2008 was multiplied as described in Chapter 5.3.1, the estimated number of collision candidates raised to as seen from Table 21. It is 164 % more than the estimated number of collision candidates in If causation factors are assumed to remain the same until 2015, the estimated collision frequency raises by 189 %. According to the analysis, the same waterways and waypoints are the riskiest than in 2008 (Figure 43). However, the risk level of the waterways to Kotka (the number 1 in Figure 43) and St. Petersburg (6 in Figure 43) decreases relatively. Table 21. The estimated number of ship-ship collision candidates in Ship-ship collision Collision candidates Change from 2008 candidate type per year Overtaking % Head-on % Crossing % Merging % Bend % Total % Bend collision candidate remains as the most common collision candidate type (42 %) and merging collision candidate as the least common type (3.7 %) as seen in Table 21. However, the number of merging collision candidates increases the most (314 %). The number of head-on collision candidates is estimated to increase the least (69 %). The traffic is increasing the most on the main route trough the Gulf of Finland where traffic to opposite directions is clearly separated. Thus the number of head-on collision candidates does not increase as much as the number of other collision candidate types. Cargo vessels remain the most common striking (75 %) and struck (72 %) candidates. The probability of an oil tanker to be a striking candidate increases to 16 % and the probability of a struck candidate to be an oil tanker increases to 19 %. However, the 76

77 possible increase of other traffic than cargo vessels and oil tankers was not taken into account in the scenario. Figure 43. Indication of the number of collision candidates in waterways and bend, merging, and crossing locations in Scale from yellow to red is used: red signifies the highest number of collision candidates in the analysed area Discussion The estimate for 2015 is made with the average growth scenario of Kuronen et al. (2008). The probability of that scenario is 50 %. Though, especially Russian ports have ambitious expansion plans. If all of them are realised, traffic will increase significantly more than in the average growth scenario used in this thesis. It is good to notice also that the possibility of low growth is 35 % (Kuronen et al. 2008). The uncertainty of the result for 2015 is rather high. The locations of the waterways and distribution of ship length were assumed to remain the same. The increase of the number of passenger ships, other ships, high speed crafts, and chemical and gas tankers navigating in the Gulf of Finland was not considered. The impacts winter has on collision probability were excluded. Cargo and oil multipliers were created in a simple way. The intention of the made calculation is to give an idea how much the probable traffic growth influences marine safety in the Gulf of Finland. The increase of 164 % of overall number of collision candidates is significant. Later on, SAFGOF proect will study and model how and how much legislation and other management actions could diminish collision probability and the consequences of collisions (Klemola et al. 2009). 77

78 6 Conclusions Analytical maritime accident probability models were presented and analysed in this thesis. The focus was on IWRAP, on its accident models, and on comparing them to other models. Ship traffic was studied in the Gulf of Finland in Calculations about collision and grounding frequency in the gulf were made. The yearly probability of collision of 0.26 was obtained for the open sea area of the Gulf of Finland in The main route from the west to St. Petersburg is the riskiest waterway. The waterway gets narrower towards the east and the probability of collision is the highest in eastern parts. Surprisingly, the crossing area between Helsinki and Tallinn did not seem particularly prone to collisions. The variation of monthly collision probability is high according to the analysis. The yearly grounding probability of ship traffic at western side of Kotka was The probability of running aground was almost equally spread to all islands and depth curves included in the analysis. Results were studied critically and sensitivity analyses were made. The magnitude of the results appeared to be in accordance with accident statistics. However, the sensitivity of the models and the uncertainty of the input parameters cause the uncertainty of the results to be significant especially in the grounding analysis. However, the uncertainty of results is a common issue in marine accident probability analyses. A challenge in the future is to develop models that are less sensitive to the uncertainty of the inputs. Specific cargo vessel and oil tanker multipliers were introduced based on traffic estimates for Multipliers were used to estimate the non-ice-related number of collision candidates in the Gulf of Finland. A raise of 164 % of collision candidates was obtained. Collision frequency is sensitive to the growth of traffic volume and thus the accident probability is estimated to increase significantly as traffic volume grows in the future. This has to be concerned when means to increase safety of marine transportation are prepared. The accident probabilities in the Gulf of Finland have not been analysed before in the same extent than in this thesis. The work should continue to include ice season and archipelago areas which were excluded from this study. In addition, less sensitive 78

79 ways to model accident frequency should be created and researched to get more accurate results. 79

80 7 Future Research Topics 7.1 Accident Frequency Models of IWRAP The models IWRAP uses still need further research to get more accurate results than what was accessible in this thesis. In the case of the collision modelling, especially the bend collision model needs to be researched more. The percentage of ships that do not turn at a bend should be paid attention to. Data about the actual traffic can be used for estimating more accurately the percentage of ships that miss a turn. For the grounding model, a specific causation factor is needed for the Gulf of Finland or preferably even several causation factors for different sea areas of the gulf. It is as important as to acquire knowledge about choosing the value for the parameter mean time between checks. 7.2 Collision Frequency Modelling in Winter No model for collision frequency in ice has been published by However, collisions are the most common in February and March (Kuala et al. 2009). In one third of ship-ship collisions between 1997 and 2006 the primary cause has been conditions outside the ship and those collisions occurred between January and March (Kuala et al. 2009). In , 56 % of 25 ship-ship collisions occurred in icebreaker assistance and 28 % in ice channel (FMA 2008). Therefore the importance of ice conditions to the ship-ship collision frequency is significant in the Gulf of Finland. However, collisions in ice conditions usually cause relatively small damages due to low speed of ships (FMA 2008). Neither collisions with ice-breakers or in convoys following an ice-breaker cannot be modelled with existing collision models. One generalising approach to the problem would be to analyse the number of collisions with ice-breakers relative to the amount of traffic during the year in question and study if a dependency could be found. Deviation of the dependence would be remarkable due to significant deviation in ice cover extent and ice season length but maybe a dependency could be found. Then a rough estimate about the number of collisions with ice-breakers could be added to the otherwise evaluated number of collisions. The collisions with ice-breakers could be estimated to be located in relation to the ice-breaking need in waterways. 80

81 The average number of days per month when ships have to navigate in narrow ice channel in the considered waterway could be researched. Thus separate causation factors could be defined for the average ice period and for the ice-free period. Different traffic volumes and traffic distributions across waterways of the mentioned seasons should be taken into account as well. This approach would increase the work of analyzing the appropriate values for causation factors as they would need to be analysed separately for different waterways and seasons. As well the geometrical collision frequency would have to be calculated separately for ice season and ice-free season. 7.3 Future Ship Traffic Estimates Kuronen et al. (2008) have prepared a detailed analysis about change in cargo transportation from 2007 to However, no work has been published concerning how many, what kind of, and how large ships will navigate in the Gulf of Finland in the future. The growth of cargo volume is not likely to produce equal growth of the number of ships due to, e.g., the introduction of larger ships. Rough estimates of future traffic were used in this thesis as no more accurate information was available. In order to make more accurate estimations about the change of risk level in the Gulf of Finland, the work mentioned above is needed. In addition, more information about, e.g., the growth of support ship traffic in relation to cargo traffic and the growth of passenger traffic in the future are needed. 81

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87 Ylitalo, J Ship-Ship Collision Probability of the Crossing Area between Helsinki and Tallinn. A student research proect. Helsinki University of Technology, Espoo, Finland. 26 p. 87

88 Appendix A: Development Suggestions for IWRAP Traffic distributions across waterways Traffic distributions have to be specified per waterway. It is not possible to take into account how ship type or draught affects the traffic distribution across the waterway of different ship groups. Especially when analyzing grounding probability this is not a good approach because ships with larger draught are navigating closer to the central line of waterway than small vessels in shallow straits. Thus, the first development suggestion is: 1. Enable the definition of different traffic distributions across waterways by ship size. AIS data The current version of the program is not capable of analyzing AIS data although it is recommended to be used to get needed traffic information. Analyzing large amounts of AIS data and preparing all needed information for each waterway and waypoint requires efforts. The utilization of IWRAP would require significantly less work time if the data handling was automatic: 2. Include the possibility to insert AIS data in a way that the program could obtain automatically the needed traffic information. Average distance between position checks by the navigator Not much information about choosing the value for average length between position checks by the navigator is available. However, results are sensitive to this parameter value (see Chapter and Ylitalo 2009). Very few used parameter values are published at all even though the model is used in several publications. Yet, the information about used parameter values and reasoning why those values have been chosen would facilitate the introduction of grounding frequency modelling to different sea areas around the world. Open discussion would be valuable in order to improve the quality of safety research and to get more valid results from grounding frequency modelling. 88

89 3. More information about choosing the value for the parameter average distance between position checks by the navigator. Area traffic When modelling accident frequencies of areas as large as the Gulf of Finland, the amount of area traffic is not the same throughout the area as has to be assumed in IWRAP 4. To be able to define different intensity of area traffic to different parts of the analysis area. Causation factor Global causation factors should be saved with the proect. 5. Save global causation factors with the proect. Traffic volume Traffic volumes have to be entered as yearly numbers of ship movements of each ship class. For example, in the case of the Gulf of Finland, traffic volumes and traffic distributions across waterways are not similar around the year as ships have to navigate in ice and mostly in darkness in winter. Thus, risk analysis has to be made separately at least for summer and winter circumstances, maybe also to spring and autumn circumstances. So it would be good if accident probabilities may be calculated also with monthly traffic volumes. For example, a selection whether to use yearly or monthly number in calculations could be added to the global settings. With current version, it is possible to insert monthly traffic volume and multiply it by twelve by using traffic volume adustment but as a consequence, results have to be divided by twelve to get monthly results. It is not possible to directly calculate with monthly traffic volume as the intensity of traffic affects collision frequency. 6. Add a possibility to make calculations with monthly traffic volumes. When inserting speeds of ship groups by hand, the decimal zeros have to be deleted one at a time as it is not possible to overwrite them as it is possible to overwrite integer numbers. This feature is impractical and causes unnecessary work. 7. When inserting traffic speeds, it should be possible to overwrite decimal numbers without deleting them first. 89

90 Maps and depth curves To facilitate grounding frequency analysis, IWRAP could define grounds and depth curves automatically from a map. 8. Define automatically grounds and depth curves from a map. Collision frequency per leg IWRAP calculates collision frequencies per leg. Does this mean that by splitting a straight waterway into two parts the waterway seems relatively less risky on the results map? 9. Give results also per length unit of leg rather than only per leg. Traffic volume adustment factor Traffic volume adustment factor may be defined to the whole model or ust to a certain direction on a certain waterway. The most detailed possibility is to define the factor for a specific ship type to specific direction on a specific waterway. If the traffic volume adustment factors are changed, it is easy to get confused which traffic volume factors have been changed and which have not. A traffic volume adustment factor overview of the whole model would be practical. A possibility to reset all traffic volume adustment factors of the model to 1 could be added as well. 10. Add a traffic volume adustment factor overview and a possibility to reset all traffic volume adustment factors of the model to 1. In addition, information about traffic volume adustment is lacking from user manual (Sonne Ravn et al. 2009). 11. Add information about traffic volume adustment to user manual. Sensitivity analysis Making sensitivity analysis is an important part of any modelling process. The current version of IWRAP does not encourage users to assess the uncertainty of the results. For instance, a feature to adust traffic distributions across all waterways could be added to study how much the uncertainty of distributions affects the results. Another example is how much varying the draughts of ships affect the results of grounding frequency analysis as precise information about actual draughts of ships is difficult as 90

91 registers store only maximum draught of ships and voyage related data fields of AIS data are not updated as often as should. 12. Add tools for sensitivity analysis. Practical issues Working with the program often includes testing the same model with different input, for example, different causation factors or different traffic adustment factors. At the current version, it is possible to give describing names to obs when they are started but it is not possible to comment them afterwards. It would be a good addition as sometimes input is not exactly as thought and had to be adusted. In addition, afterwards it is impossible to see which choices were made for each ob. It would be practical to comment them and thus be more aware of which results were the correct and interesting ones and which were not. 13. Make possible to add comments to a ob when it has already been run. If the user changes manually average speeds of vessel groups in Traffic Volume Distributor Editor, the changes do not disappear even if the user pushes cancel to close the window. 14. Changes made in Traffic Volume Distributor Editor should change if cancel is pushed. At present, it is possible to define leg-to-leg traffic so that 100 % of traffic from the same leg continues to several other legs. 15. If it is manually specified that 100 % of traffic continues to a specified leg, the traffic to all other legs should be automatically assumed to be 0 %. 16. When a proect is saved with a new name, it should be shown at the top of the window instead of the old name. 91