selected problems of marine traffic risk modelling
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Selected problems of maritime traffic risk modelling
Stockholm, 28-29 January 2010
Pentti Kujala, Professor
Jakub Montewka, Ph.D., Chief Mate
Aalto University, Finland
Przemysław Krata, Ph.D.
Maritime University of Gdynia, Poland
Selected problems of maritime traffic risk modelling
Agenda
Risk modelling - outline
Probability of an accident
Consequences
A case study
Selected problems of maritime traffic risk modelling
Risk modelling outline
P C R
P – accident‟s probability
C – accident‟s consequences
R – risk
Selected problems of maritime traffic risk modelling
Risk modelling outline
Accident probability
• Ship-ship collision
• Ship – fixed object collision
• Grounding
Accident consequences
• Oil spill from tanker
• Bunker spill from vessel
• Structural damage
• Capsizing of vessel
RISK
• Monetary terms
• Human loss
• Environmental loss
Selected problems of maritime traffic risk modelling
Accident‟s probability assessment
Ship-ship collision models
Fujii, Macduff, 1974
Pedersen, 1995
MDTC based model, 2010
Ship-fixed object collision models
Gluver&Olsen „98
U. Kunz, 1998
M. Knott, 1998
Z. Prucz, 1998
Grounding models
Fujii, Macduff, 1974
Kite – Powell, 1999
Fowler, 2000
Quy, 2007
Gravity model, 2010
Selected problems of maritime traffic risk modelling
Collision probability assessment – MDTC based model
Figure
The relationships between
MDTC, safe passing
distance, and collision
Figure
Representation of vessels
as discs and definition of
collision situation.
Selected problems of maritime traffic risk modelling
Collision probability assessment – MDTC based model
Figure
Values of MDTC obtained for all meeting scenarios,
with corresponding values of collision diameters.
Figure
MDTC and CD’s values computed at 95%
confidence level by use of Monte Carlo simulations.
0
1
2
3
4
5
10 30 50 70 90 110 130 150 170
Angle of intersection (deg)
MD
TC
(LO
A)
Tanker_Tanker
Tankers_cd
Tanker_Pass
Tanker_Pass_cd
Pass_Cont
Pass_Cont_cd
Cont_diff
Cont_diff_cd
RoRo_RoRo
RoRo_cd
Cont_Cont
Cont_cd
Tankers_diff
Tankers_diff_cd
Pass_Pass
Pass_Pass_cd
0
1
2
3
4
5
6
0 20 40 60 80 100 120 140 160 180
Angle of intersection (deg)
MD
TC
(LO
A)
1_port_2_stb Both_to_port CD
Selected problems of maritime traffic risk modelling
Collision probability assessment – causation factor
Selected problems of maritime traffic risk modelling
Grounding probability assessment – gravity model
),,( ),,(),( meRij dRTSS
),,,( )','()','()','()','( ijijijij csbHPP
The field of characteristics of ships location:
The field of characteristics of the obstructions:
T - maximum draught of a ship,
R - turning circle radius,
d - coefficient of the effective distance of obstruction detecting
e - coefficient describing a technical equipment of a ship,
m - coefficient of ship‟s manoeuvrability,
j, i - denotes coordinates of ship.
H - water depth,
s - coefficient of soundings accuracy,
b - coefficient of ship‟s hull destruction when contacted with the seabed,
c - coefficient of soundings position accuracy.
Selected problems of maritime traffic risk modelling
Grounding probability assessment – gravity model
The grounding threat intensity at any arbitrarily chosen point of the space
containing any number of sources of a threat (eg.: shallows) can be obtained as
a vector sum of grounding threat intensities coming from every single obstruction
according to the formula:
Ē(j,i) - is a grounding threat intensity field in the point (j, i),
Ē k - is a grounding threat intensity vector generated by k-numbered obstruction,
np - is a number of obstructions located in considered area.
pn
k
kEijE1
),(
Selected problems of maritime traffic risk modelling
Grounding probability assessment – gravity model
Centre of fairway Blue means safety
A spatial distribution of values of the grounding threat intensity vectors.
A shape of a safety contour (blue) depends on the assumption regarding the acceptable value of
the grounding threat intensity vectors in the closest point of shallow approach.
The critical value adjustment was performed on the basis of a minimum under keel clearance
(UKC) requirement.
Selected problems of maritime traffic risk modelling
Accident‟s consequences assessment
Quantity of oil spill
IMO methodology
MEPC 117(52) 2004
MEPC 110(49) 2003
Smailys & Česnauskis, 2006
In house build model based on the two above mentioned,
2009
Cost of oil spill
Etkin, 2000
Skjong et al. 2005
in SAFEDOR project
Yasuhira, 2009
Structural damage
Pedersen, 1994
Brown, 2002
Zhang,
In house build model, based on
Zhang‟s approach and AIS data2010
Ship capsizing
Munif et al. 2005
Bulian et al. 2009
Hinz, 2010
Selected problems of maritime traffic risk modelling
Accident‟s consequences assessment
Size of an oil outflow due to collision and grounding considering there is a spill as
a function of cargo deadweight as calculated by IMO probabilistic methodology for
double hull tankers only.
Grounding
Collision
Selected problems of maritime traffic risk modelling
Accident‟s consequences assessment
Tanker's DWT as a function of her length
y = 0,0015x3,3008
R2 = 0,9577
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
50 70 90 110 130 150 170 190 210 230 250 270 290
Length (m)
DW
T (
ton
s)
0
100
200
300
400
500
600
Nu
mber
of ship
s
Gas Crude oil Oil products Chemical
Monthly tanker traffic profiles
Winter SummerTanker
0
50
100
150
200
250
300
350
mode max min
Leng
th (
m)
Gas Crude oil Oil products Chemical
Accident‟al oil outflow model for double hull tankers in the Gulf of Finland
Selected problems of maritime traffic risk modelling
Accident‟s consequences assessment
Pareto2(9009.10; 1.90) Shift=+3.04 X > 34485
5.0%
0,0E+00
5,0E-05
1,0E-04
1,5E-04
2,0E-04
0 10000 20000 30000 40000 50000
Spill size [t]
Pro
babili
ty
Pareto2(49459; 8.4) Shift=-3.16X > 21125
5.0%
0,0E+00
5,0E-05
1,0E-04
1,5E-04
0 10000 20000 30000 40000 50000
Spill size [t] P
robabili
ty
The probability of an oil spill from the tankers operating in the Gulf of Finland in case of collision,
estimated by Pareto2 distributions for summer (to left) and winter traffic (to right).
Accident‟al oil outflow model for double hull tankers in the Gulf of Finland
Selected problems of maritime traffic risk modelling
A case study
Block diagram of risk assessment process applied in the study.
Selected problems of maritime traffic risk modelling
A case study
1. Helsinki-Tallinn crossing for summer and winter traffic.
2. Approach to oil terminal in Sköldvik
Selected problems of maritime traffic risk modelling
A case study
Winter
Mean=0.14
Summer
Mean=0.19
X <0.43
95%
0
0,2
0,4
0,6
0,8
1
0 0,25 0,5 0,75 1
RISK [USD*Million]
Pro
babili
ty
Cumulative density functions of risk due to tankers collisions in the Helsinki-
Tallinn crossing for summer and winter traffic.
Lognorm(123682; 246804) Shift=-1123.4
Mean = 122559
X > 444368
5.0%
0
0,2
0,4
0,6
0,8
1
0 0,1 0,2 0,3 0,4 0,5 0,6
RISK [USD*Millions]
Pro
babili
ty
Selected problems of maritime traffic risk modelling
The safety contours of the analyzed fairway to Sköldvik (red and green curves)
and the fairway centre line (black straight line).
60,06
60,07
60,08
60,09
60,1
60,11
60,12
60,13
60,14
60,15
25,5 25,52 25,54 25,56 25,58 25,6 25,62
Longitude [deg E]
Latitu
de [
deg N
]
The safety contours
Histograms of tankers' lateral distribution on fairway
leading to Sköldvig
0,0000
0,0005
0,0010
0,0015
0,0020
-750 -500 -250 0 250 500 750 1000 1250
Distance from waterway center [m]
Pro
babili
ty
S_bound N_bound
Two histograms of tankers‟ lateral distribution on the fairway to
Sköldvig, red line represents north bound traffic whereas black
line is south bound traffic
A case study
Selected problems of maritime traffic risk modelling
Lognorm(123682; 246804) Shift=-1123.4
Mean = 122559
X > 444368
5,0%
0,0E+00
2,0E-06
4,0E-06
6,0E-06
8,0E-06
1,0E-05
1,2E-05
0 0,1 0,2 0,3 0,4 0,5 0,6
RISK [USD*Millions]
Pro
babili
ty
Lognorm(123682; 246804) Shift=-1123.4
Mean = 122559
X > 444368
5.0%
0
0,2
0,4
0,6
0,8
1
0 0,1 0,2 0,3 0,4 0,5 0,6
RISK [USD*Millions]
Pro
babili
ty
Probability and cumulative density functions of variable “risk” in case of grounding in
the Sköldvik harbour approach, summer traffic.
A case study
Selected problems of maritime traffic risk modelling
Thank you for your attention
Selected problems of maritime traffic risk modelling
Stockholm, 28-29 January 2010
Pentti Kujala, Professor
Jakub Montewka, Ph.D., Chief Mate
Aalto University, Finland
Przemysław Krata, Ph.D.
Maritime University of Gdynia, Poland
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