1

RISK ANALYSIS OF THE TRANSIT VESSEL TRAFFIC IN THE STRAIT

OF ISTANBUL

Özgecan S. Ulusçu (a), Birnur Özbaş (b),

Tayfur Altıok (c), İlhan Or (d)

(a , c) Laboratory for Port Security, Rutgers, The State University of New Jersey,

Piscataway, NJ 08854, USA, ozgecanu@eden.rutgers.edu

(b , d) Boğaziçi University, Department of Industrial Engineering,

34342, Bebek, Istanbul, TURKEY, birnur@ozbas.com.tr

June 2009

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ABSTRACT

The Strait of Istanbul, the narrow waterway separating Europe from Asia, holds a strategic importance in

maritime transportation as it links the Black Sea to the Mediterranean. It is considered as one of the

world’s most congested and difficult-to-navigate waterways. Over 55,000 transit vessels pass through the

Strait annually, roughly 20% of which carry dangerous cargo.

In this study, we have analyzed safety risks pertaining to transit vessel maritime traffic in the Strait of

Istanbul and proposed ways to mitigate them. Safety risk analysis was performed by incorporating a

probabilistic accident risk model into the simulation model. A mathematical risk model was developed

based on probabilistic arguments regarding instigators, situations, accidents, consequences and historical

data as well as subject-matter expert opinions. Scenario analysis was carried out to study the behavior of

the accident risks, with respect to changes in the surrounding geographical, meteorological and traffic

conditions.

Our numerical investigations suggested some significant policy indications. Local traffic density and

pilotage turned out to be two main factors affecting the risks at the Strait of Istanbul. Results further

indicate that scheduling changes to allow more vessels into the Strait will increase risks to extreme levels.

Conversely, scheduling policy changes that are opted to reduce risks may cause major increases in average

vessel waiting times. This in turn signifies that the current operations at the Strait of Istanbul have reached

a critical level beyond which both risks and vessel delays are unacceptable.

Keywords: Risk Analysis, Risk Evaluation, Expert Judgment, Simulation, Maritime Traffic, Strait of

Istanbul

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1 INTRODUCTION

The Turkish Straits, which consist of the Strait of Istanbul and Çanakkale and the Sea of Marmara, have

for centuries been one of the world’s most strategic waterways since they constitute the Black Sea's sole

maritime link to the Mediterranean and the open sea beyond. As such, they are a vital passageway not just

for trade but for the projection of military and political power, while their extreme narrowness, winding

contours and densely populated shores make navigation quite treacherous in these waterways.

The Strait of Istanbul is approximately 31 km long, with an average width of 1.5 kilometers and a mere

660m at its narrowest point. It features many sharp turns, forcing the ships to alter course at least 12 times,

some necessitating turns of up to 80 degrees. Additionally, frequent adverse meteorological conditions,

such as dense fogs and high currents and winds, contribute to the complexity of navigation in the Strait.

Figure 1. The Strait of Istanbul

There are also some non-natural factors making navigation through the Strait of Istanbul hazardous. One

of them is the dense local traffic, such as intra-city passenger boats, fast ferries, fishing boats, pleasure

boats etc. [VTS User Guide, 2004]. Another important non-natural factor that negatively effects

navigation in the Strait is the frequency and cargo characteristics of transit vessels. About 56,600 vessels

(10,000 being dangerous material carriers) traveled through the Strait of Istanbul in 2007.

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The legal framework of the transit traffic through the Strait of Istanbul is governed by the 1936 Montreux

Convention [Montreux Conv., 1937]. When the Convention was put in effect, less than 5,000 vessels used

to pass through the Strait annually, whereas today, changes in international shipping patterns and volume

have led to a ten-fold increase. Additionally, during the 1930s, transport of hazardous materials posed

little concern due to the infrequent passages and small vessel sizes. However, the increases in traffic and

vessel sizes have raised the likelihood and potential severity of accidents. The unusual characteristics of

the Strait of Istanbul, coupled with the failure to request pilotage in this treacherous waterway, have led to

over 200 accidents during the past decade.

Some major accidents have occurred since 1960, when the Greek-flagged M/T World Harmony collided

with the Yugoslavian-flagged M/T Peter Zoranic, leading to the death of 20 crew members, severe oil

pollution and fire that lasted several weeks, suspending the transit traffic. In 1979, Romanian-flagged

Independenta and the Greek freighter M/V Evriyalı collided at the southern entrance of the Strait. 43 crew

members died, 64,000 tons of crude oil spilled into the sea and 30,000 tons burned into the atmosphere. In

yet another catastrophe, the Greek Cypriot vessels M/T Nassia and M/V Shipbroker collided in the Strait.

29 officers and crewmen perished and 20,000 tons of crude oil burned for five days, suspending the traffic

for a week [Turkish Maritime Pilots, 2005].

In order to control and mitigate maritime accident risks in the described dire environment and ensure the

safety of navigation, life, property and environment, the Turkish State unilaterally adopted in 1994 (and

later revised in 1998) the Maritime Traffic Rules and Regulations (R&R) for the Turkish Straits Region

[Official Gazette, 1998]. Then, in 2003, the Bureau of Turkish Strait’s Vessel Traffic Services (VTS) has

been set up and empowered to administer the R&R, through a sophisticated Vessel Traffic Control &

Monitoring System, (covering not only the Strait, but also 20 miles into the Black Sea and the Sea of

Marmara).

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Even though the number of accidents decreased after the adoption of the R&R, the vulnerability of the

Straits was evident once again in a 1999 incident; Voganeft-248, a Russian tanker, ran aground and broke

apart at the Southern entrance of the Strait. About 150,000 tons of oil spilled into the sea, and clean-up

efforts lasted several months [Birpınar et al, 2005].

The goal of this research is to analyze the risks involved in the transit vessel traffic in the Strait of Istanbul

and provide suggestions to reduce safety risks. We have carried out a model-based mathematical risk

analysis to be used in the risk mitigation process to improve safety in the Strait. In the first step of the risk

analysis, the transit vessel traffic system in the Strait of Istanbul was thoroughly analyzed and a simulation

model was developed to mimic and study maritime operations and surrounding environmental conditions.

In addition to transit vessel traffic through the Strait and geographical conditions, the current vessel

scheduling practices were modeled using a scheduling algorithm [Uluscu et al., 2009].

Risk analysis of the Strait was performed by incorporating a probabilistic accident risk model into the

simulation model. Probabilistic arguments utilized historical accident data, as well as subject-matter

expert opinions. Then a scenario analysis was performed in order to study the behavior of accident risks

and arrive at some critical policy suggestions. This analysis allowed us to investigate the impact of various

factors on the risk profile of the Strait. These factors included vessel arrivals, scheduling policies, pilotage,

overtaking, and local traffic density.

2 LITERATURE ON MARITIME RISK ANALYSIS

The existing risk analysis literature in maritime systems mainly focuses on probabilistic risk analysis

arguments, simulation modeling and statistical analysis of data. Below, we present a brief overview.

[Amrozowicz, 1996] and [Amrozowicz et al., 1997] focus on the determination of the probability of oil

tanker grounding using fault-tree analysis. [Dougligeris et al., 1997] provides a methodology of

quantifying and assigning risk cost estimates in maritime transportation of petroleum products. Similarly,

[Iakovou, 2001] considers the maritime transportation of crude oil and petroleum products using a multi-

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objective network flow model allowing routing strategies for risk mitigation. [Slob, 1998] presents an

optimizing study to minimize spills in the Dutch inland waterways.

[Harrald et al., 1998] describes the modeling of human error related accident event sequences in a risk

assessment study of maritime oil transportation in Prince William Sound, Alaska. [Merrick et al., 2000]

and [Merrick et al., 2002] present a detailed simulation model of the Prince William Sound oil

transportation system developed primarily for risk mitigation purposes. [Merrick et al., 2001] discusses

the Washington State Ferries Risk Assessment. A modeling approach was developed combining

simulation, expert judgment and risk analysis. [Van Dorp et al., 2001] describes a study that has been

carried out to assess the sufficiency of passenger and crew safety in the Washington State ferry system,

while focusing on the estimation of the level of risk present and possible risk reduction measures. As a

supplement to [Merrick et al., 2001], the potential consequences of collisions are modeled to determine

the requirements for onboard and external emergency response procedures and equipment. [Merrick and

Van Dorp, 2006] combines a Bayesian simulation of the occurrence of situations with accident potential

and a Bayesian multivariate regression analysis. [Kuroda et al., 1982] proposes a mathematical model for

estimating the probability of the collision of vessels passing through a uniform channel. [Kaneko, 2002]

considers probabilistic risk assessment methods to estimating the probability of collision. [Yip, 2008]

studies port traffic risks in Hong Kong by using a negative binomial regression model and historical data,

and provides a statistical evidence that the port of registration, the vessel type and the accident type are

critical to the number of injuries and fatalities.

Literature also includes work based on statistical analysis of data to model accident probabilities and

casualties. [Maio et al., 1991] developed a regression model to estimate the waterway casualty rate

depending on the geographic conditions. [Roeleven et al., 1995] describes approaches to obtain a

forecasting model for the probability of accidents as function of waterway attributes and circumstances.

[Anderson and Talley, 1995] studies causal factors of oil cargo spills, and tanker barge vessel accidents,

while [Talley, 1996] investigates the main factors of the risk and the severity of container cargo accidents.

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Similarly, [Psaraftis et al., 1998] presents an analysis on the factors that are important determinants of

maritime transportation risk. [Le Blanc and Rucks, 1996] describes the cluster analysis performed on a

sample of over 900 vessel accidents that occurred on the lower Mississippi River. [Kite-Powell et al.,

1998] developed physical risk model is based on a set of risk factors, including human error and vessel

characteristics. [Yudhbir and Iakovou, 2005] presents the development of an oil spill risk assessment

model. [Moller et al., 2005] reviews the current status of the government-industry partnerships for dealing

with oil spills as the result of maritime transportation. [Talley, 2008] discusses issues surrounding

maritime safety and security in ports and coastal waterways.

The early work in Strait of Istanbul is somewhat limited. [Kornhauser and Clark, 1995] used the

regression model developed by [Maio et al., 1991] to estimate the vessel casualties resulting from

additional oil tanker traffic through the Strait of Istanbul. [Otay and Özkan, 2003] developed a simulation

model to estimate the probability distribution of vessel casualties using the geographical characteristics of

the Strait of Istanbul. [Tan and Otay, 1998] and later [Tan and Otay, 1999] present a physics-based

stochastic model to investigate casualties resulting from tanker accidents in the Strait of Istanbul. [Or and

Kahraman, 2002] investigates possible factors contributing to accidents in the Strait of Istanbul using

Bayesian analysis and simulation modeling. [Akten, 2004] studies maritime accidents in the Strait of

Istanbul and states that current, daytime and location are the most important factors affecting casualties.

3 A MATHEMATICAL RISK MODEL

As mentioned earlier, numerous instigators have been witnessed resulting in an accident or a closure while

a transit vessel navigates in the Strait of Istanbul. For example, there can be a mechanical failure in the

vessel, or the captain or the pilot can make an error. These causal occurrences that may trigger an accident

are referred to as instigators. They include human error, rudder failure, propulsion failure, communication

and/or navigation equipment failure, and mechanical and/or electrical failure. Clearly, the occurrence of

an instigator depends on the situation, which may be represented by a vector of situational attributes.

Typical accidents that may occur in the Strait include collision, grounding, ramming, sinking and fire

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and/or explosion. Accidents may occur in chain in such a way that an accident may cause another

accident. 1st tier accident types include collision, grounding, ramming and fire and/or explosion, while the

2nd tier accident types (that may occur following a 1st tier accident) include grounding, ramming, fire

and/or explosion, and sinking. Potential consequences of the 1st and 2nd tier accidents include human

casualty, property and/or infrastructure damage, environmental damage and loss of traffic effectiveness

and throughput. This framework is shown in Figure 1.

Figure 1. The framework of the risk model

Defining situations (factors and their states) that affect the likelihood and/or impact level of instigators and

accidents is critical for the intended risk analysis. Such factors, called Situational Attributes, are divided

into two groups: attributes influencing accident occurrence and attributes influencing consequences. These

two groups of situational attributes are further classified as vessel and environmental/shore attributes as

displayed in Figure 2 and 3. Vessel attributes influencing accident occurrence include class, reliability and

whether the vessel has pilot and/or tugboat. Reliability is determined by vessel’s age and its flag as

suggested by the 2006 Shipping Industry Flag State Performance Table [MISS, 2006]. Environmental

attributes include distance between transit vessels, weather and water conditions, geographical difficulty,

density of local traffic and navigating daytime or nighttime. Attributes influencing consequence impact

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also include vessel attributes such as cargo type and vessel length and shore attributes such as population,

historical structures, commercial and residential property, and infrastructure.

Figure 2. Situational attributes influencing accident occurrence

Figure 3. Situational attributes influencing the consequences

Note that in order to quantify risks, we need to answer the following questions:

• How often do the critical situations occur?

• For a particular situation, how often do instigators occur?

• If an instigator occurs, how likely is an accident?

ENVIRONMENTAL ATTRIBUTES

Vessel Class(Type & Length)

Vessel Reliability(Age & Flag)

Pilot Request

Tugboat Request

Vessel Proximity

Visibility

Current

Geographical Difficulty

Local Traffic Density

ATTRIBUTES INFLUENCING ACCIDENT OCCURRENCE

VESSEL ATTRIBUTES

SHORE ATTRIBUTES

Vessel Class(Type & Length)

Population

Historical Buildings

Property

Infrastructure

ATTRIBUTES INFLUENCING CONSEQUENCE IMPACT

VESSEL ATTRIBUTES

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• If an accident occurs, what would the damage to human life, property, environment and

infrastructure be?

In this study, risks are quantified based on historical data, expert judgment elicitation and a simulation

model of the transit vessel traffic in the Strait of Istanbul. 2005 and 2006 data acquired from the VTS

include inter-arrival time, speed, pilot request percentage, tugboat request percentage, anchorage

percentage, anchorage duration, stopover percentage, age and flag of different classes of vessels (nearly

for 100.000 vessels). The local traffic schedules for the ferries, motorboats, and tourist boats obtained

from the websites of companies. 1988-2005 visibility data were obtained from the Kandilli Observatory

and Earthquake Research Institute [KOERI, 2005]. 1991-2005 visibility data obtained from the

International Weather Information Website [WUG, 2005], Surface current data were obtained from VTS

and the Department of Navigation, and Oceanography of the Turkish Navy [SHODB, 2005]. The

simulation model first generates arrivals of vessels, according to classes shown in Table I along with a

number of vessel attributes, using probability distributions based on these data. It then schedules vessel

entrances and administers their pilotage needs, navigation through the Strait (with such details as speed

and overtaking) and their exit with all the relevant local traffic, weather and current conditions. All these

processes are designed and executed in line with the R&R, while the scheduling algorithm closely mimics

the practices and conventions of the VTS (regarding decisions on sequencing vessel entrances in daytime

and nighttime, as well as start time and duration of the northbound, southbound and bi-directional travel

time-windows. The model was validated using vessel transit times and scheduling decisions made by the

operators in some days of 2005. The results were highly satisfactory [Uluscu et al., 2009]. Finally accident

data obtained from VTS, Turkish Undersecretariat for Maritime Affairs and [Ece, 2007] for last 60 years

were used for the calibration process.

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Table I. Vessel types in the Strait of Istanbul for scheduling purposes

Length (m.)

Draft (m.) Tanker Carrying

Dangerous Cargo LNG-LPG Dry Cargo Passenger Vessels

< 50 < 15

50 - 100 < 15

100 - 150 < 15

150 - 200 < 15

200 - 250 < 15

250 - 300 > 15

> 300 > 15

Type

Class D

Class P

Class T6

Class C

Class EClass B

Class C

Class A

In the model, the Strait of Istanbul is divided into 21 slices (each 8 cables long, where 1 cable = 0.92

miles) for risk presentation purposes, as depicted in Figure 4. The risk in slice s, ( )sR S , is computed

based on the snapshot (defined by S ) of the slice every time a vessel enters it (in either direction). The

snapshot contains information about all the situational attributes (vector S ) as observed by the entering

vessel. First, the risk contribution of the vessel entering the slice is computed and then the contribution of

each vessel in the snapshot is computed and all are aggregated into the slice risk in a cumulative manner.

Since there are about 55,000 transits annually, such snapshots of instantaneous risks are taken that many

times in each slice. Accordingly, even though instantaneous risks are not continously tracked and

recorded, the sampling of these risks (accomplished at each vessel entrance) is sufficiently random and

numerous to provide a healthy analysis. Also, note that an alternative approach would be to take

snapshots of every slice at every properly chosen t∆ time units. Both approaches can be successfully

applied to ports or waterways to create meaningful samples of instantaneous risks.

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Figure 4. Risk slices at the Strait of Istanbul

The risk in slice s, ( )sR S , as observed by a vessel entering slice s, is given by

( ) ( )1 2

( ) , Pr , Pr |s j vs vs vs j vs vs vssv j j

R S E C A S A S E C A S A Sϕ σ

ϕ ϕ ϕ σ σ σϕ σ∈ ∈ ∈ ∈ ∈

⎛ ⎞⎡ ⎤ ⎡ ⎤= × + ×⎜ ⎟⎣ ⎦ ⎣ ⎦⎜ ⎟

⎝ ⎠∑ ∑ ∑ ∑ ∑

V A A C C

(1)

where

vsAϕ : 1st tier accident type φ in slice s involving vessel v

vsAσ : 2nd tier accident type σ in slice s involving vessel v

1A : Set of 1st tier accident types

2A : Set of 2nd tier accident types that may be caused by 1st tier accident type φ as indicated in Table II

j vsC ϕ : Consequence type j of 1st tier accident type φ in slice s initiated by vessel v

j vsC σ : Consequence type j of 2nd tier accident type σ in slice s initiated by vessel v

ϕC : Set of consequence types of accident type φ ( 1ϕ∈A ) as indicated in Table III

σC : Set of consequence types of accident type σ ( 2σσ ∈ A ) as indicated in Table III

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sV : Set of vessels navigating in slice s, as seen by the observing vessel

sX : Vector of situational attributes affecting accident occurrence

sW : Vector of situational attributes affecting consequence of an accident

( , )s sS X W∈

Note: When there are no 2nd tier accidents, the second term in (1) disappears.

We can also interpret ( )sR S as the instantaneous risk in slice s, conditioned on the particular realization

of S at the time of a vessel entry into slice s. Thus the expected risk for slice s is obtained by averaging

( )sR S over the number of vessels that have entered slice s. Since this process is done for each slice, a

risk profile is obtained for the entire Strait at the end of a simulation run.

Table II. Causal relationship between the 1st and 2nd tier accident types

No 2nd Tier

Accident

2nd Tier Accident Type

Grounding Ramming Fire / Explosion Sinking

1st T

ier

Acc

iden

t Typ

e Collision Grounding Ramming Fire / Expl.

(For example: a grounding may either not lead to a 2nd tier accident or it may lead to fire/explosion or sinking, as a

2nd tier accident)

Table III. Set of accident consequences

Consequences

Property / Infrastructure

Damage

Human Casualty

Environmental Damage

Traffic Effectiveness

Acc

iden

ts

Typ

es

Collision Grounding Ramming Fire/Explosion Sinking

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The probability of a 1st tier accident type φ in slice s involving vessel v based on the situation vector S

observed by an entering vessel is given by

( ) ( ) ( ) ( )I

Pr | Pr | Pr , Prvs vs s vs is s is si

A S A X A I X I Xϕ

ϕ ϕ ϕ∈

= = ×∑ (2)

where,

isI : Instigator type i in slice s

ϕI : Set of instigators that may cause a 1st tier accident type φ as given in Table IV.

Table IV. Set of instigators that may cause an accident

1st Tier Accidents

Collision Grounding Ramming Fire / Explosion

Inst

igat

ors

Human Error Steering Failure Propulsion Failure Comm./Nav. Equipment Failure

Mechanical/Electrical Failure

The probability of a 2nd tier accident type σ in slice s involving vessel v is given by

( ) ( ) ( ) ( ) ( ) ( )1 1

Pr | Pr | Pr , Pr | Pr Pr | .vs vs vs vs vs vs vs vsA S A X A A X A X A A A Xσ σ σ ϕ ϕ σ ϕ ϕϕ ϕ∈ ∈

= = × ≅ ×∑ ∑A A

(3)

Note we assume that 2nd tier accidents depend only on the 1st tier accidents and not the situation vector S .

The expected consequence j in slice s given 1st tier or 2nd tier accident type φ or σ are respectively given by

( ), , Pr ,h hj vs vs j vs vs s j vs j vs vs s

h LE C A S E C A W C C A Wϕ ϕ ϕ ϕ ϕ ϕ ϕ

∈

⎡ ⎤⎡ ⎤ = = ×⎣ ⎦ ⎣ ⎦ ∑ (4)

( ), , Pr ,h h

j vs vs j vs vs s j vs j vs vs sh L

E C A S E C A W C C A Wσ σ σ σ σ σ σ∈

⎡ ⎤⎡ ⎤ = = ×⎣ ⎦ ⎣ ⎦ ∑

(5)

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where,

L : Set of impact levels of which h is the index (low, medium, high)

hj vsC ϕ : Consequence type j at level h of 1st tier accident type φ in slice s contributed by vessel v

hj vsC σ : Consequence type j at level h of 2nd tier accident type σ in slice s contributed by vessel v.

Clearly, conditional probabilities, marginal probabilities and conditional expectations are involved in the

expressions (1) - (5). Among them, ( )Pr ,vs is sA I Xϕ and ( )Pr is sI X

(for human error),

( )Pr ,hj vs vs sC A Wϕ ϕ

and ( )Pr ,hj vs vs sC A Wσ σ are all obtained using expert judgment, and ( )Pr is sI X (for

instigators other than human error) and ( )Pr vs vsA Aσ ϕ

are obtained from historical data.

During this study, a total of 53 questionnaires were answered by numerous experts with different

backgrounds and the average response times varied from hours to days depending on their availabilities.

Also, consistencies of the experts’ answers are double checked and maintained.

• A questionnaire for every Accident Type/Instigator Combination:

– A total of 14 questionairres;

– 32 to 40 questions in each questionnaire.

• Regarding Human Error Instigator:

– 1 questionnaire;

– 40 questions.

• A questionnaire for every Accident Type/Consequence Level Combination:

– A total of 38 questionnaires;

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– 8 to 20 questions in each questionnaire.

• Interviews with 23 Experts:

– 5 Academicians (ITU School of Maritime Affairs)

– 10 Pilot Captains

– 3 VTS Marine Traffic Experts

– 5 Open Sea Captains (from Private Sector)

3.1 Probability of a First Tier Accident

In order to compute the probability of a 1st tier accident in a slice based on the situation vector sX , one

needs to evaluate (2) involving,

i) probability of a 1st tier accident given an instigator and a situation,

ii) probability of an instigator given sX .

In addition to these evaluations, a calibration process needs to be carried out to make sure that the long-

run accident probabilities are legitimate.

3.1.1 Probability of a 1st Tier Accident Given an Instigator and a Situation

Due to lack of data to determine the contribution of various situational attributes to accident risks,

estimation of the conditional probability of an accident given an instigator requires elicitation of expert

judgment.

In this research, “a paired-comparison elicitation method” [Cooke, 1991] is deployed in the extraction of

expert judgment. This is because experts are regarded to be more comfortable in making paired

comparisons, rather than directly assessing a probability value for a given situation. This approach was

also used by [Merrick et al., 2001], [Merrick and Van Dorp, 2006] and [Szwed et al., 2006].

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The paired-comparison approach focuses on the functional relationship between situational attributes

1 2( , , , )TpX X X Xϕ

= … where pφ is the number of situational attributes influencing occurrence of 1st tier

accident φ the given accident occurrence) and an accident probability rather than a value function. The

probability of a 1st tier accident given an instigator and the situation vector sX can be written as,

( ) ( )Pr , expTi iA I X P Xϕ ϕ ϕβ= (6)

where iϕβ is the vector of parameters and iPϕ is the calibration constant for the 1st tier accident type φ

and instigator type i. The accident probability model (6) is also used by [Roeleven et al., 1995], [Merrick

et al., 2000] and [Van Dorp et al., 2001]. It is based on the proportional hazards model originally proposed

by [Cox, 1972], which assumes that accident probability behaves exponentially with changes in covariate

values.

The probability of an accident defined by (6), where [0,1]pX ∈ , i pRϕβ ∈ and [0,1]iPϕ ∈ , is assumed to

depend on the situational attributes listed in Table V. A cardinal ranking is obtained through expert

opinion to create a consistent scale for each situational attribute. This is accomplished by asking each

expert to provide a cardinally ordered weigts for all categories of each situational attribute iX .

Finally, the averages of such weights over all experts constitute the cardinal ranking of the described

levels of each attribute. The values of iX are normalized so that 1iX = describes the “worst” case

scenario, while 0iX = describes the “best” case scenario. For example, regarding the 11th attribute, (time

of the day), 11 1X = represents the nighttime, while 11 0X = represents the daytime. The possible

settings of the situational attributes influencing accident occurrence are listed in Table V.

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Table V. Possible settings of situational attributes influencing accident occurrence

Variable Attribute Name Number of

Possible Settings

Description

1X 1st Interacting Vessel Class 9 Vessel types in Table I rearranged

from 1 to 9

2X 2nd Interacting Vessel Class 11 Similar to 1X , including 2 types of

local vessels

3X 1st Vessel Tugboat Request 2 Yes, No

4X 1st Vessel Pilot Request 2 Yes, No

5X

Nearest Transit Vessel Proximity

(from a given transit vessel)

9

same direction 0-4 cables, same direction 4-8 cables; same direction >8

cables, 1 knot/hr speed difference overtaking lane, 2 knots/hr speed

difference overtaking lane, 3 knots/hr speed difference overtaking lane, 4

knots/hr speed difference overtaking lane, opposite direction normal lane, opposite direction overtaking lane

6X Visibility 3 <0.5 mile, 0.5-1 mile , >1 mile

7X Current 8

0-2 knots/hr same direction with 1st vessel, 2-4 knots/hr same direction with 1st vessel, 4-6 knots/hr same

direction with 1st vessel, > 6 knots/hr same direction with 1st vessel, 0-2 knots/hr opposite to 1st vessel, 2-4 knots/hr opposite to 1st vessel, 4-6 knots/hr opposite to 1st vessel, > 6

knots/hr opposite to 1st vessel

8X Local Traffic Density 3 1-2, 3-5, >5

9X Zone 12

Anadolu Feneri-Sarıyer SB, Anadolu Feneri-Sarıyer NB, Sarıyer-Beykoz SB, Sarıyer-Beykoz NB, Beykoz-Kanlıca SB, Beykoz-Kanlıca NB,

Kanlıca-Vaniköy SB, Kanlıca-Vaniköy NB, Vaniköy-Üsküdar SB,

Vaniköy-Üsküdar NB, Üsküdar-Kadıköy SB, Üsküdar-Kadıköy NB

10X Vessel Reliability 9 Age (New, Middle Age, Old) x Flag Category (Low Risk, Medium Risk,

High Risk)

11X Time of the Day 2 Daytime, Night time

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In addition to the individual situational attributes listed in Table V, attributes describing interaction effects

are included in the model. For example, 12 1 9X X X= ⋅ represents the interaction between the 1st

interacting vessel class and the zone. The aim is to capture the combined impact of certain key attributes

on the accident probability. There are 12 interaction attributes as seen in Table VI.

Table VI. Interaction attributes

Interaction Description

12X 1 9X X⋅ 1st Interacting Vessel Class x Zone

13X 4 7X X⋅ 1st Vessel Pilot Request x Current

14X 4 9X X⋅ 1st Vessel Pilot Request x Zone

15X 3 9X X⋅ 1st Vessel Tugboat Request x Zone

16X 3 7X X⋅ 1st Vessel Tugboat Request x Current

17X 5 6X X⋅ Nearest Transit Vessel Proximity x Visibility

18X 5 7X X⋅ Nearest Transit Vessel Proximity x Current

19X 7 9X X⋅ Current x Zone

20X 6 8X X⋅ Visibility x Local Traffic Density

21X 6 9X X⋅ Visibility x Zone

22X 9 8X X⋅ Zone x Local Traffic Density

23X 10 4X X⋅ Time of the Day x 1st Vessel Pilot Request

To assess the accident probability given an instigator, subject matter experts are asked to compare a series

of situation pairs ( 1 2,X X ) differing only in one attribute (regarding their ratio of probabilities to cause an

accident). If the expert thinks that the likelihood of an accident is the same in both situations, then he/she

circles “1”. Otherwise, the expert circles a value towards the likelier event (the higher ratio of the

probability, the higher the circled value). Figure 5 provides a sample question from one of the related

questionnaires.

For each 1st tier accident, a separate questionnaire was prepared containing 4 questions per attribute (one

question representing the worst case scenario, one representing the best case, and two others

corresponding to ordinary cases).

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Figure 5. A sample question for impact of instigators on collision probability

Let ,e qz be the response of expert e ( 1, ,e r= … ) to question q ( 1, , 4q pϕ= … ), where r is the total

number of experts and 4pφ is the total number of questions in each questionnaire. Then, we can write

( )( )

( )( ) ( )( )11

, 1 22 2

expPr ,exp

Pr , exp

T

T

T

i iqq i

e q q qi iq q

P XA I Xz X X

A I X P X

ϕ ϕϕϕ

ϕ ϕ ϕ

ββ

β= = = − (7)

where ( )1 2q qX X− denotes the difference vector of the two situations evaluated by the differences of

cardinality ratings assigned to attribute settings. Therefore, given an instigator, the ratio of accident

probabilities in two situations depends only on the difference vector ( )1 2q qX X− and the parameter

vector iϕβ . Since the experts are asked to assess the ratio in (7), the parameter vector i

ϕβ can be estimated

using these difference vectors and without determining the accident probability itself.

21

The aggregate expert responses are taken as their geometric mean:

1

,1

r r

q e qe

z z=

⎛ ⎞= ⎜ ⎟⎝ ⎠∏ . (8)

Then, linear regression can be deployed to determine the relative effect of the situational attributes on the

accident probability. In other words,

( )1 2

Tiq q q qy X Xϕβ ε= − + (9)

where ln( )q qy z= and qε is the residual error.

Under the assumption that qε is normally distributed (ε q ∼ σ 2. . (0, )i i d N ), (9) can be regarded as a set

of linear regression equations, where aggregate expert responses are the dependent variables,

1 2( )q qX X− are the independent variables and }{ ,0 ,1 ,2 ,: , , ,......,i i i i ipϕϕ ϕ ϕ ϕ ϕβ β β β β are the regression

parameters, to be estimated for each (accident type - instigator) pair. The R values associated with 9 are

between 0.955 and 0.985 for all accident type-instigator pairs. As an example normal probability plot of

residuals and residuals vs. predicted variable (collision-human error) pair are given in Figure 6. Also

Figure 7 shows contribution of each attribute and their interactions for the collision probability given a

human error as an instigator as a result of this regression.

22

Normal Prob. Plot

-1.2-1.0-0.8-0.6-0.4-0.20.00.20.40.60.8

-1.0 -0.5 0.0 0.5 1.0

Normal Distribution

Res

idua

ls

Residuals vs Pred Y

-1.2-1.0-0.8-0.6-0.4-0.20.00.20.40.60.8

-2 -1 0 1 2 3

Predicted Y

Res

idua

ls

Figure 6. Normal probability plot of residuals and residuals vs. predicted variable of (collision-human

error) pair

‐5

‐4

‐3

‐2

‐1

0

1

2

3

4

5

X1 X2 X3 X4 X5 X6 X7 X8 X10 X15 X16 X19 X20 X21 X22

β Coefficients

Figure 7. β coefficients for impact of human error on collision probability

3.1.2 Probability of an Instigator Given a Situation

Due to the lack of data regarding the occurrence of human error in accidents in the Strait, expert judgment

is deployed to derive conditional human error probabilities, while historical accident data is used to derive

occurrence probabilities of the remaining instigators. Clearly, occurrence of human error is affected by the

particular vessel attributes, as well as environmental attributes. So, its conditional probability is estimated

using the paired-comparison approach described earlier. Thus, the probability of human error is written as,

23

( ) ( )Pr Human Error exp TiX P Xα= (10)

where iP is the calibration constant and α is the parameter vector for the human error probability. To

assess the conditional human error probability, experts were asked to compare a series of situation pairs

( 1 2,X X ). Figure 8 displays a sample question from the human error questionnaire, which consists of 40

questions.

Figure 8. A sample question representing the impact of situational attributes on human error

The regression equation describing the relative effect of situational attributes on human error probability is

given by,

( )1 2T

q q q qy X Xα ε= − + (11)

where qε is the residual error. Again, under the assumption that qε is normally distributed, (11) can be

regarded as a set of linear regression equations, and the parameter vector α can be obtained by solving it.

Figure 9 shows contribution of each attribute and their interactions for the human error probability.

24

Figure 9. α coefficients for human error probability

Let us now go back to the probability of a 1st tier accident based on the situation vector sX as observed by

a vessel entering a slice. Recall that the accident probabilities are used to compute slice risks at points of

vessel entry into slices. As an example, consider the probability of collision:

( ) ( ) ( )Pr Collision,HumanError| Pr Collision HumanError, Pr HumanErrors s sX X X= × (12)

Combining (2), (6), (10) and (12) and all the instigators, we can write,

( )

( )

( )

1

1

1 1

2 2 21 1, 1,0

1

3 3 31 1, 1,0

1

1 1 11 1, 1,0 1 1, 1,0

1 1

4 41 1,

Pr Collision in slice |

exp Pr Steering Fail

exp Pr Propulsion Fail

exp exp

exp

s

p

i i si

p

i i si

p p

i i i ii i

i i

s X

P x X

P x X

P x P x

P x

β β

β β

β β α α

β

=

=

= =

=

+ + ×

+ + ×

+

⎛ ⎞ ⎛ ⎞+ × +⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠⎛ ⎞⎜ ⎟⎝ ⎠⎛ ⎞⎜ ⎟⎝ ⎠

∑

∑

∑ ∑

( )1

41,0

1Pr Comm/NavFail

p

si

Xβ=

⎛ ⎞+ ×⎜ ⎟

⎝ ⎠∑

(13)

25

where each term of the summation represents the joint probability of collision and an instigator, given sX .

We can use (13) in evaluating (1) provided all the calibration constants and the conditional probabilities

are known.

3.1.3 Calibration

Since coefficient iϕβ , for accident ϕ and instigator i, of the conditional probability function (6) are

obtained through the pair-wise comparison process, a further calibration of the constants iP and iPϕ is

necessary to make sure that values generated by (6) will correspond to their counterparts obtained from

historical data. For calibration purposes, we first let 1 41 1 1 1P P P= = = = and

( ) ( ) ( )Pr SteeringFail Pr Propulsion Fail Pr Comm/NavFail 1s s sX X X= = =

in (13) and make a

preliminary run of the simulation model. Averaging vessel observations of each component of (13) in the

long run provides us with the joint probabilities of accident types and instigators conditioned on the

aforementioned parameters being 1. Note that this process removes the condition on the situation

vector sX . Then, these values are compared with their counterparts (e.g. ( )Pr Collision, Human Error ,

etc.) obtained from the historical data. Additionally, we have,

( ) ( ) ( )Pr Collision,Human Error Pr Human Error Collision Pr Collision= × (14)

Using historical data, we can estimate ( )Pr Human Error Collision through,

( ) Number of collisions due to human errorPr Human Error Collision

Total number of collisions,= (15)

and approximate the probability of a vessel being involved in a collision by

( ) Number of vessels involved in collisionsPr A vessel involved in a collision

Total number of vessels.= (16)

26

Then, ( )Pr Collision,Human Error can be estimated using (14) – (16).

Let the long run unconditional P(Collision,Human Error) that

is

P11 exp β

1,i1 x

ii=1

p1

∑ + β1,01

⎛

⎝⎜⎞

⎠⎟× P

1exp α

1,ix

ii=1

p1

∑ +α1,0

⎛

⎝⎜⎞

⎠⎟× Pr X

ls( )l∈X∑ in (13) be 1

1 1 1P P C , which reduces to C1

since 11P and 1P are set to unity in the preliminary run of the model. Thus, the comparison of

( )Pr Collision,Human Error with its counterpart obtained from the historical data, will provide an estimate

for the product of calibration constants 11 1P P through

( )1

1 11

Pr Collision,Human Error.P P

C= (17)

Similarly, for the remaining joint accident and instigator probabilities, we will use

( ) ( )21

2

Pr Collision,SteeringFailPr SteeringFail ,P

C× = (18)

( ) ( )31

3

Pr Collision,Propulsion FailPr Propulsion Fail ,P

C× = (19)

and

( ) ( )41

4

Pr Collision,Comm/NavFailPr Comm/NavFail .P

C× = (20)

Note we made an approximation here by assuming that instigators do not depend on the situational

attribute vector sX . This was necessary since data did not provide joint probabilities of instigators and

situations. One is now ready to compute conditional as well as marginal accident and instigator

probabilities (used in (2)), and obtain the probability of a first tier accident.

27

3.2 Probability of a Second Tier Accident

The conditional probability of a 2nd tier accident given the occurrence of a 1st tier accident is estimated

using the historical accident data as shown below:

( )st nd

st

Number of type 1 tier accidents that lead to a type 2 tier accidentPr

Total number of type 1 tier accidents.A Aσ ϕ

ϕ σϕ

= (21)

Values of ( )Pr A Aσ ϕ for the Strait are given in Table VII.

Table VII. Values for Pr (2nd tier Accident|1st tier Accident)

2nd tier Accident

No 2nd Tier

Accident Grounding Ramming Fire /

Explosion Sinking

1st ti

er A

ccid

ent Collision 0.8737 0.0289 0.0000 0.0158 0.0816

Grounding 0.9794 0.0041 0.0165

Ramming 0.8325 0.1218 0.0102 0.0355 Fire /

Explosion 0.9355 0.0081 0.0000 0.0565

3.3 Expected Consequence Given an Accident

For slice risk computation, we next need to obtain the expected consequence of accidents that may be

caused by the situations observed by an entering vessel, that is, j vs vsE C Aϕ ϕ⎡ ⎤⎣ ⎦ and j vs vsE C Aσ σ⎡ ⎤⎣ ⎦ ) as

presented below.

3.3.1 Probability of a Consequence Given an Accident

Due to the lack of any consequence data, expert judgment is relied upon in the estimation regarding the

probability of specific impact level realizations. It is assumed that the probability of impact level depends

on accident type and situational attributes. The list of considered situational attributes influencing impact

level and their possible settings are given in Table VIII.

28

As seen in Table VIII, the 1st interacting vessel type has three different settings for different consequence-

accident type pairs. For example, regarding (environmental damage, collision) pair, this attribute has five

possible settings in terms of cargo type and amount; while regarding (human casualty, collision) pair, it

has three settings based on the number of people in the vessel.

Table VIII. Possible settings of situational attributes influencing consequence impact

Variable Attribute Name

Number of

Possible Values

Description

1W 1st Interacting Vessel Type

6 LNG-LPG, Tanker, Empty LNG-LPG, Empty Tanker; Passenger,

other vessel 2 Passenger vessel, other vessel

3 Loaded LNG-LPG and Tanker, Passenger, other vessel

2W 2nd Interacting Vessel Type

6 LNG-LPG, Tanker, Empty LNG-LPG, Empty Tanker; Passenger,

other vessel 2 Passenger vessel, other vessel

3 Loaded LNG-LPG and Tanker, Passenger, other vessel

3W 1st Interacting Vessel Length 2 0-150m., 150-300m.

4W 2nd Interacting Vessel Length 2 0-150m., 150-300m.

5W Zone 6

Anadolu Feneri-Sarıyer, Sarıyer-Beykoz, Beykoz-Kanlıca,

Kanlıca-Vaniköy, Vaniköy-Üsküdar, Üsküdar-Kadıköy

6W 1 2W W⋅ 1st Interacting Vessel Type x 2nd Interacting Vessel Type

7W 3 4W W⋅ 1st Interacting Vessel Length x 2nd Interacting Vessel Length

8W 1 5W W⋅ 1st Interacting Vessel Type x Zone

9W 3 5W W⋅ 1st Interacting Vessel Length x Zone

The conditional probability of consequence impact level is estimated using the paired comparison

approach described earlier. Thus, the conditional probabilities of impact level of consequence type j given

an 1st tier or 2nd tier accidents are given by φ or σ and the situation vector W,

29

( ) ( ) ( ) ( )Pr , exp Pr , expT Th h h h h h

j j j j j jC A W G W or C A W G Wϕ ϕ ϕ σ σ σγ γ= = (22)

where hjGϕ and h

jGσ are calibration constants and hjϕγ and h

jσγ are the parameter vectors. To assess the

probability of impact level given an accident, experts are asked a series of questions, each comparing two

situations 1W and 2W (varying from one another in a single attribute), for each impact level. Table IX

displays the general descriptions provided to the experts regarding impact levels, so that experts’

perceptions of the terms deployed (such as low/medium/high severity) would not vary greatly. Figure 10

displays a sample question from the questionnaire for the (fire/explosion, human casualty) pair. A separate

questionnaire is prepared for each (consequence, accident type) pair, where four questions are asked per

situational attribute (one question representing the worst case scenario, one representing the best case

scenario, and two others corresponding to intermediate scenarios).

Figure 10. A sample consequence question

Similar to (9), the resulting set of regression equations used to determine the relative effect of the

situational attributes on the probability of impact levels given an accident are,

30

( ) ( )1, 2, 1, 2,

T Th hq j q q q q j q q qy W W or y W Wϕ σγ ε γ ε= − + = − + (23)

where qε is the residual error.

Since the hjϕγ

and hjσγ parameters of the conditional probability functions (22) are derived through the

aforementioned relative comparison process, a further calibration of the constants h

jGϕ and hjGσ is

necessary. We have used the historical accident data in this calibration process. In this regard, the number

of past accidents which received negligible coverage, some coverage, headline coverage in the media

(regarding their consequences) have been deployed to calibrate the conditional probability functions

associated with accident consequences (in terms of low, medium, high impact levels).

Conditional probabilities of consequences can be computed in a manner similar to the ones used in

computing accident probabilities. As an example, consider the probability of low casualty given collision,

that is,

( )( ) ( )( )11

1 1 111 11, 11,0

1

Pr Casualty Low Collision , Pr Casualty Low Collision ,

exp .

s ls

p

i ii

W W

G xγ γ=

=

⎛ ⎞= +⎜ ⎟

⎝ ⎠∑

(24)

Finally, the conditional probabilities of consequences are normalized to make

sure ( ) ( )Pr 1 Pr 1L L

h hj j

h handC A C Aϕ ϕ σ σ

∈ ∈= =∑ ∑ .

3.3.2 Consequence Impact Level

It is assumed that the quantitative values of impact levels of a consequence type j, ( hj vsC ϕ or h

j vsC σ ) are

uniformly distributed within pre-specified ranges, as displayed in Table IX. These values/ranges

(representing the user’s perception of a low, medium and high consequence) do not represent the actual

impact of an accident in a specific unit (e.g. dollars or number of casualties) and can be altered in a

parametric fashion in a series of scenarios, in order to study the effects of different quantification

31

conventions. As such, the computed risk values are meaningful only when compared to each other in a

given context. For example, comparing risk at different slices helps to determine high and low risk zones.

Table IX. Consequence impact levels

Impact Level Value

Property / Infrastructure

Damage

Human Casualty

Environmental Damage

Traffic Effectiveness

Low Uniform (0-1,000) Small boats, tugs, fishing nets, shore

No death No spills No closures

Medium Uniform (4,000-6,000)

1-2 vessels , ferries,

breakwater, cables 1 death Less than

1000 tone Less than 4

hours

High Uniform (8,000-10,000)

Ports, historical buildings, waterside

residences, valuable goods,

More than 1 death

More than 1000 tone

More than 4 hours

Then, the conditional expectation of consequences are obtained using (4) - (5), which in turn allows us to

compute the expected total consequence that is nothing but the risk we have been striving to compute from

the very beginning.

4 OBSERVATIONS AND CONCLUSIONS

We have experimented with the aggregate simulation/risk model described earlier with the parameters

(such as, vessel arrival rates, overtake and pursuit distances, vessel entrance schedules and local traffic

density, among others.) reflecting the current situation in the Strait, based on year 2005-2006 data. The

risk profiles of this “base scenario” (in terms of average slice risks and average maximum instantaneous

risks), obtained using 25 replications - each of one year length, are displayed in Figure 11. The average

slice risk profile exhibits a steady behavior from the north entrance all the way down to the Bogazici

Bridge, where the local traffic congestion starts showing itself in this highly populated and busy region of

the Strait. Interaction of the transit and local traffic patterns generates a large spike in the average risk in

Slice 19 (i.e. the area of the Strait corresponding to downtown Istanbul and including the main harbor

region) and tapers off around the south entrance. The average maximum instantaneous risk profile also

32

exhibits a similar behavior varying from 100 to 850 fold differences between average risks and average

maximum instantaneous risks observed at various points along the Strait. This remarkable observation

indicates how risky the maritime traffic in the Strait of Istanbul can get at specific instances. That is,

depending on random realizations of accident causing factors, ordinary and safe appearance of the Strait

maritime activity could swiftly change into a very risky environment. For example, a rare realization

observed in Slice 1 (corresponding to risk value 12210) involved an excessive level of fog during

nighttime and two D-class vessels that just entered the slice before the Strait is closed. Another rare

realization, observed in Slice 19 (corresponding to instantaneous risk value 10710), involved an A-vessel

that was about to leave the Strait just after the night schedule started, a D-vessel and an E-vessel along

with 10 local vessels. Such potentially highly dangerous situations may be rare, but a rare disaster is a

disaster too many. So, high risks indicated by the maximum risks should be taken seriously.

Figure 11. Current risk profiles of the Strait of Istanbul

33

It can be seen in Figure 12 how the southbound traffic which is mostly of the laden cargo, varies over time

and generates significantly higher instantaneous risks in slices 19 and 20.

Figure 12. 3D Diagram for all southbound vessel types through 12 days of the year

Using the aggregate model, we have performed an extensive scenario analysis to investigate the

characteristics of accident risks in the Strait under different settings and conditions. This analysis has

provided us with the ability to observe and predict how changes in various policies and practices impact

the risk profile of the Strait. Below we summarize our observations.

Observation 1

The accident risks in the Strait and the average vessel waiting times exhibit a tight balance. For instance,

a small increase in arrival rates may result in rather high waiting times at the entrances (an increase of

60% for some vessel classes). Furthermore, scheduling changes made to reduce vessel waiting times

increase risks in the Strait substantially. Conversely, one has to be very careful in revising the scheduling

mechanism for the purpose of risk mitigation since the waiting times are highly sensitive to entrance rules.

The benefits obtained in risks may not justify the resultant waiting times. In the future, scheduling changes

may be justified, if significant reductions occur in the transit vessel traffic, perhaps due to alternative oil

Slice Risk

Slice

Time of Day

34

transport modes such as pipelines and other routes. Thus, scheduling decisions to balance out delays vs.

risks should be made based on extensive experimentation with the model developed in this study.

Observation 2

The model indicates that pilots are of utmost importance for safe passage, and lack of sufficient pilotage

service significantly increases the risks in the Strait. Currently, vessels longer than 250 m. are mandated to

take a pilot, and it is voluntary for the rest. As a result of our experimentation, we have recommended

mandatory pilotage for vessels longer than 150 m. This will reduce the average risk by 7%, the average of

maximum risk by 11% in Slice 19 and the observed maximum instantaneous risk is 11114 observed in

Slice 3 (6763-fold of its average). Had pilotage been obligatory for vessels longer than 100 m., this would

reduce the average risks by 46 % and the average of maximum risks by 33 % at Slice 19.

Observation 3

Even though current regulations discourage overtaking anywhere in the Strait, results indicate that

overtaking a vessel is less riskier as opposed to requiring a pursuing faster vessel to slow down behind a

slower vessel, where the average slice risk and the average of maximum risk are increased by 28 % and 21

% in Slice 19, respectively. In the latter case, the maximum observed instantaneous risk is 23030 (12633-

fold of its average) observed in Slice 1. Therefore, in the regions where the geography of the Strait

tolerates it, overtaking seems to be a safe practice (as also suggested by the expert opinion).

Observation 4

The most significant contributor to risk appears to be the juxtaposition of the transit vessel traffic and the

local traffic. When the local traffic density in the Strait is decreased by 50% during daytime, it results in

an 83% decrease in the average risk and 31% decrease in the average maximum instantaneous risk of Slice

19. Accordingly, for potential risk mitigation, the scheduling procedure maybe revised to enable more

effective night-time traffic where there is almost no local traffic. The difference between risks involved in

day time and night time traffic is depicted in Figure 13 that is demonstrating risks of all southbound cargo

35

for 24 hours from midnight, averaged over a year. Clearly, this issue requires further research regarding

the kind of modifications that can be done to the scheduling practice to accommodate a larger volume of

night-time traffic, hopefully without increasing overall vessel delays or other risks.

Figure 13. Risks in Slice 19 of all southbound cargo averaged over a year

The nature of the global economy and international politics dictate that the maritime transit traffic in the

Strait of Istanbul cannot be greatly reduced, nor eliminated. Nonetheless, the economic and political

realities versus environmental awareness and risk management need not to be mutually exclusive goals in

the Strait. The risks regarding the transit traffic can be mitigated by operational policies and rules that

adequately regulate and guide the transit traffic, while maintaining the freedom of passage. Until then, the

environment, the priceless historical/cultural heritage and the health and safety of the city’s residents will

be at jeopardy.

In fact, the balance between economic and political realities and concerns for risks (leading to various

rules, regulations and requirements) are common in all constrained maritime environments (and especially

in narrow waterways). Furthermore, it would neither be unrealistic, nor difficult to project the main

Slice Risk

Time of Day

36

observations and policy suggestions made for the Strait of Istanbul to other maritime environments in a

general qualitative sense. However, in order to make any quantitative assessment of the risks involved, a

similar extensive risk study should be performed in each case, for which this study can only act as a guide.

Acknowledgement: Throughout this study, we have received sincere collaboration from the Turkish

Straits Vessel Traffic Services (VTS), Turkish Ministry of Transportation Directorate General of Coastal

Safety, Turkish Undersecretariat for Maritime Affairs, Turkish Maritime Pilots’ Association, private

industry, Istanbul Technical University Faculty of Maritime, Bogazici University Kandilli Observatory

and Earthquake Research Institute, Turkish Navy Office of Navigation, Hydrography and Oceanography

for which we are utmost thankful. We are also thankful to Prof. Johan René van Dorp of George

Washington University for his valuable suggestions in this study. This work is in part funded by the

Laboratory for Port Security at Rutgers University, NSF Grant Number INT-0423262, and TUBITAK,

The Scientific and Technological Research Council of Turkey through the Research Project 104Y207 and

BAP (Scientific Research Projects Fund of Bogazici University) through the Research Project 09A301D.

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