A SYSTEMS APPROACH TOWARDS
RISK INTERVENTION PRIORITIZATION
IN MARITIME ENVIRONMENT
Dr. T.A. Mazzuchi,
Dr. J.R. van Dorp,
Dr. J.R. Harrald
Dr. J Merrick (VCU)
Dr. M. Grabowski (RPI)
Engineering Management &
Systems Engineering Department
1
THE RISK OF RIVER BOAT GAMBLING
A Risk Assessment for the
Port of New Orleans Port Authority
“Are you odds of winning better
than your odds for dying”
Joint Work:
The George Washington University
Rensselaer Polytechnic Institute
2
The Prince William
Sound Risk Assessment
A Risk Assessment for
ADEC, APSC/SERVS,
PWS Regional Citizens Advisory Council,
US Coast Guard, PWS Shipping Companies)
Joint Work:
Det Norske Veritas
The George Washington University
Rensselaer Polytechnic Institute
The Washington State
Ferry Risk Assessment
Washington State Department of Transportation
The George Washington University
Rensselaer Polytechnic Institute
Virginia Commonwealth University
The San Francisco Bay
Traffic Density Analysis
San Francisco Bay Ferry Associations
The George Washington University
Virginia Commonwealth University
Citations of Work Presented
“A Bayesian paired comparison approach for relative accident
probability assessment with covariate information”, European Journal of
Operational Research, Vol. 169, Issue 1, 2006, pp. 157-177.
“A traffic density analysis of proposed ferry service expansion in San
Francisco Bay using a maritime simulation model”, Reliability
Engineering and System Safety, Vol. 81, Issue 2, 2003, pp. 119-132.
"The Prince William Sound risk assessment", Interfaces, Vol. 32, No.
6, 2002, pp. 25-40.
“A risk management procedure for the Washington State Ferries",
Risk Analysis, Vol. 21, 2001, pp. 127-142.
“Risk modeling in distributed, large scale systems", IEEE
Transactions in Systems Man, and Cybernetics Part A: Systems and
Humans, Vol. 30, 2000, pp.651-660.
“A systems approach to managing oil transportation risk in Prince
6
William Sound", Systems Engineering, Vol. 3, 2000, pp. 128-142.
Examples Risk Intervention Questions
• Port of New Orleans Risk Assessment:
“Is it safer for a gambling boat to be underway or at the dock?”
• Prince William Sound Risk Assessment:
“Should we tighten weather based closure restrictions for outbound tankers?”
• Washington State Ferry Risk Assessment:
“Is it (cost\risk) efficient to invest in addition survival craft capacity on
Washington State Ferries?”
• San Francisco Ferry Analysis:
“Can the current system maintain an acceptable risk level under future
modifications and projected traffic increase”
7
Maritime Accidents
•
•
•
•
•
•
•
COLLISION
POWERED GROUNDING
DRIFT GROUNDING
ALLISION
FOUNDERING
STRUCTURAL FAILURE
FIRE\EXPLOSION
Stakeholders
• Shipping (Oil/Passenger) Companies
• US Coast Guard
• US Departments of Transportation, Commerce, Interior
and Environmental Protection Agency
• Local Port Authorities
• Fishing Industry
• Pleasure Craft Industry
• Environmentalist Groups
• Local Community
A Risk Assessment Approach for Dynamic
Transportation Systems
Organizational
Risk Factors
High
Performing
Low
Performing
Risk Averse
Risk Prone
Organizations
Organizations
Situational
Risk Factors
High Risk
System States
Low Risk
System States
• Organizational Risk Factors - influence the likelihood of the
occurrence of triggering events.
• Situational Risk Factors - influence the likelihood of occurrence
of accidents given the occurrence of a triggering event.
The Dynamic Risk Profile of the System
• The situational and organizational factors lead to the dynamic profile
of system risk.
RISK
10-2
10-3
10-4
10-5
10-6
10-7
TIME
FIGURE 3: Dynamic Risk Profile
• The peak risk spikes in the system may to 100 to 1000 times riskier
than the average system risk level.
• Identifying how and when these risk spikes occur is a fundamental
objective of the dynamic risk assessment methodology.
The Maritime Accident Event Chain
Stage 1
Basic/Root
Causes
E.g.
Inadequate Skills,
Knowledge,
Equipment,
Maintenance,
Management
Stage 2
Immediate
Causes
E.g.
Human Error,
Equipment Failure,
Stage 3
Incident
Stage 4
Accident
E.g.
E.g.
Loss of Power,
Collisions,
Loss of Steering ,
Groundings,
Dangerous Navigation Fire/Explosion
ORGANIZATIONAL FACTORS
Vessel type
Flag/classification society
Vessel age
Management type/changes
Pilot/officers on bridge
Vessel incident/accident history
Individual/team training
Safety management system
Stage 5
Consequence
Stage 6
Delayed
Consequence
E.g.
Oil Outflow,
Persons in Peril
E.g.
Environmental
Damage,
Loss of Life
SITUATIONAL FACTORS
Type of waterway
Wind Speed
Traffic situation
Wind Direction
Traffic density
Current
Visibility
Time of day
Risk Reduction Interventions
Stage 1
Basic/Root
Causes
E.g.
Inadequate Skills,
Knowledge,
Equipment,
Maintenance,
Management
Stage 2
Immediate
Causes
E.g.
Human Error,
Equipment Failure,
Risk Reduction/
Prevention
1. Decrease
Frequency of
Root/Basic
Causes
Stage 3
Incident
E.g.
E.g.
Loss of Power,
Collisions,
Loss of Steering,
Groundings,
Dangerous Navigation Fire/Explosion
Risk Reduction/
Prevention
2. Decrease
Frequency
Immediate
Causes
Stage 4
Accident
3. Decrease
Exposure to
Hazardous
Situations
Stage 5
Consequence
Stage 6
Delayed
Consequence
E.g.
Oil Outflow,
Persons in Peril
E.g.
Environmental
Damage,
Loss of Life
Risk Reduction/
Prevention
Risk Reduction/
Prevention
4. Intervene to
Prevent Accident
if Incident Occurs
5. Reduce
Consequence
(Oil Outflow)
if Accident Occurs
Risk Reduction/
Prevention
6. Reduce Impact if
Oil Outflow Occurs
E.g.
E.g.
E.g.
E.g.
E.g.
E.g.
Closure
Conditions,
Inspection
Program,
Emergency
Repair
or
Double
Hull,
Pollution
ISM,
One-way Zone,
Double Engine,
Assist Tug,
Double Bottom Response Vessel,
Training,
Traffic
Sep.
Scheme,
Double Steering,
Emergency Response
Oil Boom,
Better
Traffic
Management,
Redundant
Nav
Coordination,
Pollution
Maintenance
Nav. Aids for Poor
Aids,
VTS Watch
Response
Visibility
Work Hour Limits,
Coordination
Drug/Alcohol Tests
Data and The Maritime Accident Event Chain
Stage 1
Basic/Root
Causes
Stage 2
Immediate
Causes
Stage 3
Incident
SPARSE DATA
Stage 4
Accident
Stage 5
Immediate
Consequence
DATA BASES
Stage 6
Delayed
Consequenc
e
Modeling the Causal Chain: Collision Risk
Vessel
Attributes
Waterway
Attributes
Opportunity
for Incident
Incident
Pr(OFI)
Pr(Incident|OFI)
Simulation +
Counting Model
Collision
Pr(Collision|Incident,OFI)
Data on technological
failures Expert Judgement
on Human Error
Data + effect of
waterway attributes
from expert judgment
Information Flow Prince William Sound Simulation
Quest. I & II
Vessel Ops.
Questionnaires
Quest. III & IV
Failure/Error
Questionnaires
Determination of Relative
Incident Probabilities
Quest. V & VI
Calibration
Questionnaires
Vessel Reliability
& Appropriate
Incident Data
Simulation
Weather Data
Traffic Data
System Description
Calibration of Vessel
and Situational Relative
Incident Probabilities
Characterization
of PWS Accident
Profiles
Modeling Traffic Movements
Published Data
VTS Way Point Data
17
Modeling Traffic Movements
Rules of the Road
Nuisance Traffic:
Fishing Openers and Regattas
18
Modeling Rules of the Road and Weather
(PWS Risk Assessment)
19
Continuous vs. Discrete System
Risk
PWS OFI = 5 minutes
WSF OFI = 2.5 minutes
SFF OFI = 1 minute
Time
20
Interacting Vessels
21
OFI Counting
Opportunity For Incident
10-mile radius
probability density
2-mile radius
2 miles
D
10 miles
Distance
22
Not Every Interaction is the Same
1 Ferry, 1 Container Vessel
Crossing Tracks
Scenario 1
Ferry
2 Ferries, Parallel Tracks
Scenario 2
Container Vessel
23
OFI Counting Model
FRONT
CROSSING
BACK
PASSING
PASSING
(OVERTAKING)
(MEETING)
-
CROSSING
BACK
Vessel
Ferry
-
FRONT
24
Modeling Conditional Failure Probabilities
Using Expert Judgment
Given a propulsion Failure, Asses the likelihood of Collision
(PWS Risk Assessment)
Traffic Type: Tug with Tow
Traffic Prox.: Vessels 2 to 10 Miles
Tanker Size & Direction: Inbound more than 150 DWT
Wind Direction: Perpendicular/on Shore
Wind Speed: More than 45
Visibility: Greater than 1/2 mile
No Bergy Bits
“System States”
Bergy Bits within a mile
25
Expert Judgment Questionnaire
(Example: WSF Risk Assessment
Vessel Reliability Failure Will Lead to Collision?)
Question: 2
Situation 1
Issaquah
SEA-BRE(A)
High Speed Ferry
Crossing astern
< 0.5 miles
No Vessel
No Vessel
No Vessel
> 0.5 Miles
Along Ferry
0
9:
7:
5:
3:
1:
34
Attribute
Ferry Class
Ferry Route
1st Interacting Vessel
Traffic Scenario 1st Vessel
Traffic Proximity 1st Vessel
2nd Interacting Vessel
Traffic Scenario 2nd Vessel
Traffic Proximity 2nd Vessel
Visibility
Wind Direction
Wind Speed
Likelihood of Collision Avoidance
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
VERY MUCH MORE LIKELY to allow collision avoidance.
MUCH MORE LIKELY to allow collision avoidance.
MODERATELY LIKELY to allow collision avoidance.
SOMEWHAT MORE LIKELY to allow collision avoidance.
EQUALY LIKELY to allow collision avoidance.
Situation 2
Meeting
-
26
Example Result
Responses Question 51
Average Answer out of 6 experts : 5.23
Regression Fit: 5.29
2.5
Expert 10
Expert 9
2
# Responses
Expert 8
Expert 7
1.5
Expert 6
1
Expert 5
Expert 4
0.5
Expert 3
0
-9
-8
-7
-6
-5
-4
-3
-2
1
2
Response Values
3
4
5
6
7
8
9
Expert 2
Expert 1
27
Accident Probability Model - Regression
Traffic Scenario 1  X
1
Paired Comparison
1
2
Traffic Scenario 2  X
Pr( Accident | Propulsion Failure, X )  P0 e
1
 T X 1
 T X 1
3
Pr( Accident | Prop . Failure, X ) P0 e
 T  X 1 X 2 

e
2
 T X 2
Pr( Accident | Prop. Failure, X ) P0 e
4
 Pr( Accident | Prop . Failure, X1 ) 
LN 

2 
 Pr( Accident | Prop. Failure, X ) 
1
2
T
X
1
X
2

28
Example Regression Analysis Fit
R2 of Regressions in the order of 75% to 80%
Note: This is fit for representing expert data not fit to actual values
29
The responses can be enumerated through the
use of the exponential risk equation
Location
Central Sound
Traffic Proximity
Vessels 2 to 10 Miles
Traffic Type
Tug with Tow
Tanker Size & Direction Inbound More Than 150DWT
Escort Vessels
Two or more
Wind Speed
More Than 45
Wind Direction Perpendicular/On Shore
Visibility
Greater Than 1/2 Mile
Ice Conditions
Bergy Bits Within a Mile
LIKELIHOOD OF COLLISION
98765432123456789
No Bergy Bits in a Mile
Location
Traffic Proximity
Traffic Type
Tanker Size & Direction
Escort Vessels
Wind Speed
Wind Direction
Visibility
Ice Conditions
Enumerating the exponential yields
Relative Pr(Collision) = 313.2
Relative Pr(Collision) = 136.0
The risk model says ice in this condition is 2.3 times more dangerous
What remains is to determine the scaling factor from data
30
Simulation Analysis Tool
(PWS Risk Assessment)
31
Simulation Analysis Tool
(WSF Risk Assessment)
32
Simulation Analysis Tool
(SFF Analysis)
33
Prince William Sound Mitigation Analysis
Case A
Baseline Case
Case B, B1
MINIMUM SAFEGUARDS
Case 1
CAT I
ROOT CAUSES
Case 1.1
CAT IA & ID
H/O ERROR
Case 1.4
1A 2,3,4
BRIDGE TEAM
Case 3
CAT III
EXPOSURE
Case 2
CAT II
IMMEDIATE CAUSES
Case 1.2
CAT IB & 1C
VRF
Case 1.5
1A1
DIM. ABILITY
Case C, C1
MAXIMUM
SAFEGUARDS
Case B2
BASE CASE W/O ESCORTS
Case 1.3
CAT ID
INFORMATION
Case 3.1
III 1.2
CLOSURE
Case 3.2, 3.2A
III 1.4
ICE
Case 4, 4A
CAR IV
INTERVENTION
Case 3.3
III3
FISHING VS
Case 5A, 5B
CAT V
CONSEQUENCES
Case 4.1
IV B2
TUG
Case 4.2
IV A4
FAILURE CAPTURE
Risk Reduction Cases Analyzed
INTERVENTIONS
Stage I Interventions:
Reduce basic or root
causes.
Stage II Interventions:
Decrease frequency of
immediate (triggering)
events
Stage III Interventions:
Decrease exposure to
hazardous situations.
Stage IV Interventions:
Intervene to prevent
accidents if incidents
occur.
Stage V Interventions:
Reduce oil outflow if
accidents occur.
RISK REDUCTION CASES EVALUATED
Case
Case
Case
Case
Case
Case
Case
1. Improve human and organizational performance (SS)
1.1 Reduce human and organizational error (M/FT, SS)
1.1.1 Improve training and navigation information (SS)
1.1.2 Improve bridge team management (SS)
1.1.3 Reduce incidence of diminished ability (SS)
1.2 Reduce vessel reliability failures (M/FT, SS)
2 Improve internal vigilance. (SS)
Case 3 Reduce exposure to hazardous weather, ice, traffic conditions
(SS)
Case 3.1 Impose stricter closure conditions (M/FT, SS)
Case 3.1.1 Impose stricter closure conditions at Hinchinbrook Entrance
(SS)
Case 3.2 Revise ice navigation procedures (FT,SS)
Case 3.3 Revise fishing vessel/tanker interaction rules (SS)
Case 4 Revise escort program using pre-positioned tugs (M/FT,SS)
Case 4A Revise escort program, pre-position salvage tug at
Hinchinbrook
Entrance (M/FT, SS)
Case 4.1 Tug in indirect mode in Narrows: (FT)
Case 4.2 Capture/correct 50% of all steering and propulsion failures
(M/FT)
Case 5A Replace entire fleet with double hulled tankers.(M/FT)
Case 5B Hydrostatically load all single hulled vessels in existing fleet
(M/FT).
35
(M/FT)
Displaying Results
36
Interactions by Route and Interacting Vessel
Washington State Ferry Analysis
NON - WSF
WSF
37
Average Collision Probability per Interaction
by Route and Interacting Vessel (WSF)
NON - WSF
WSF
38
Statistical Expected Number of Collisions per
Year by Route and Interacting Vessel (WSF)
NON - WSF
WSF
39
Traffic Density Maps
San Francisco Bay Analysis
40
Traffic Density Comparisons
San Francisco Bay Analysis
SFB Alternative 3
SFB Alternative 1
41
Expected Frequency Accident Scenarios
Cumulative % of Total Accidents (PWS)
Risk Mitigation Effectiveness - PWS
Tug in Ind irect M o d e in Narro ws
Only Hyd ro st at ic Lo ad ing
Revised Esco rt & Fishing \ Tanker
Rules
Revised Fishing \ Tanker Rules
Imp ro ved Human\ Org anizat io n
Perf o rmance
Red uce Pro p . & St eer. Failure b y
50 %
Esco rt wit h Salvag e Tug
Revised Esco rt
Red uced Human Erro r
Red uced V RF (A ll t o b est )
Imp ro ved M anag ement & Crew
A d d it io nal Perso n o n t he B rid g e
St rict er Clo sure at HE
Imp ro ved Training & Navig at io n
Inf o rmat io n
Red uced Diminished A b ilit y
Only Do ub le Hull
B ase
Revised Ice Pro d ecures
Red uced Exp o sure
St rict er Clo sure
-100%
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
Ave r age % Change in # Accide nts fr om Bas e Cas e
DNV : Average % Change in # Accidents
GWU : Average % Change in # Accidents
43
Risk Mitigation Effectiveness - WSF
44
Lessons Learned
• Use of local experts is very important for acceptance
• Experts can impart useful knowledge for risk analysis
• Other data sources are always available
• In many instances risk is a dynamic function of the system
• Risk needs to be addressed system wide – avoid local focus
• Risk Management questions must be established before risk
modeling is conducted
• Each system will have a certain uniqueness and new modeling
challenges
45