Selected Problems Of Marine Traffic Risk Modelling

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


Agenda

Risk modelling - outline

Probability of an accident

Consequences

A case study


Risk modelling outline

P C R

P – accident‟s probability

C – accident‟s consequences

R – risk


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


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


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.


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


Collision probability assessment – causation factor


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.



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

),(



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.


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



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



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



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


A case study

Block diagram of risk assessment process applied in the study.


A case study

1. Helsinki-Tallinn crossing for summer and winter traffic.

2. Approach to oil terminal in Sköldvik


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


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


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


Thank you for your attention


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

Documents

Selected Problems Of Marine Traffic Risk Modelling