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1 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.

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Presentation on theme: "1 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."— Presentation transcript:

1 1 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

2 2 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

3 The Prince William Sound Risk Assessment Joint Work: Det Norske Veritas The George Washington University Rensselaer Polytechnic Institute A Risk Assessment for ADEC, APSC/SERVS, PWS Regional Citizens Advisory Council, US Coast Guard, PWS Shipping Companies)

4 The Washington State Ferry Risk Assessment Washington State Department of Transportation The George Washington University Rensselaer Polytechnic Institute Virginia Commonwealth University

5 The San Francisco Bay Traffic Density Analysis San Francisco Bay Ferry Associations The George Washington University Virginia Commonwealth University

6 6 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 William Sound", Systems Engineering, Vol. 3, 2000, pp. 128-142.

7 7 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

8 Maritime Accidents COLLISION POWERED GROUNDING DRIFT GROUNDING ALLISION FOUNDERING STRUCTURAL FAILURE FIRE\EXPLOSION

9 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

10 A Risk Assessment Approach for Dynamic Transportation Systems 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. High Performing Risk Averse Organizations Organizational Risk Factors Organizational Risk Factors Low Performing Risk Prone Organizations Situational Risk Factors Situational Risk Factors High Risk System States Low Risk System States

11 The Dynamic Risk Profile of the System The situational and organizational factors lead to the dynamic profile of system risk. 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.

12 E.g. Collisions, Groundings, Fire/Explosion Stage 4 Accident E.g. Loss of Power, Loss of Steering, Dangerous Navigation Stage 3 Incident E.g. Human Error, Equipment Failure, Stage 2 Immediate Causes ORGANIZATIONAL FACTORS Vessel typeFlag/classification society Vessel ageManagement type/changes Pilot/officers on bridgeVessel incident/accident history Individual/team trainingSafety management system SITUATIONAL FACTORS Type of waterwayWind Speed Traffic situationWind Direction Traffic densityCurrent VisibilityTime of day The Maritime Accident Event Chain E.g. Inadequate Skills, Knowledge, Equipment, Maintenance, Management Stage 1 Basic/Root Causes E.g. Oil Outflow, Persons in Peril Stage 5 Consequence E.g. Environmental Damage, Loss of Life Stage 6 Delayed Consequence

13 Risk Reduction Interventions E.g. ISM, Training, Better Maintenance Risk Reduction/ Prevention 1. Decrease Frequency of Root/Basic Causes E.g. Inspection Program, Double Engine, Double Steering, Redundant Nav Aids, Work Hour Limits, Drug/Alcohol Tests Risk Reduction/ Prevention 2. Decrease Frequency Immediate Causes 3. Decrease Exposure to Hazardous Situations E.g. Closure Conditions, One-way Zone, Traffic Sep. Scheme, Traffic Management, Nav. Aids for Poor Visibility E.g. Collisions, Groundings, Fire/Explosion Stage 4 Accident E.g. Loss of Power, Loss of Steering, Dangerous Navigation Stage 3 Incident E.g. Human Error, Equipment Failure, Stage 2 Immediate Causes E.g. Inadequate Skills, Knowledge, Equipment, Maintenance, Management Stage 1 Basic/Root Causes E.g. Oil Outflow, Persons in Peril Stage 5 Consequence E.g. Environmental Damage, Loss of Life Stage 6 Delayed Consequence E.g. Emergency Repair or Assist Tug, Emergency Response Coordination, VTS Watch Risk Reduction/ Prevention 4. Intervene to Prevent Accident if Incident Occurs E.g. Double Hull, Double Bottom Risk Reduction/ Prevention 5. Reduce Consequence (Oil Outflow) if Accident Occurs E.g. Pollution Response Vessel, Oil Boom, Pollution Response Coordination Risk Reduction/ Prevention 6. Reduce Impact if Oil Outflow Occurs

14 SPARSE DATADATA BASES Stage 1 Basic/Root Causes Stage 2 Immediate Causes Stage 3 Incident Stage 4 Accident Stage 5 Immediate Consequence Stage 6 Delayed Consequenc e Data and The Maritime Accident Event Chain

15 Data on technological failures Expert Judgement on Human Error Data + effect of waterway attributes from expert judgment Modeling the Causal Chain: Collision Risk Simulation + Counting Model Opportunity for Incident IncidentCollision Vessel Attributes Waterway Attributes Pr(OFI)Pr(Incident|OFI) Pr(Collision|Incident,OFI)

16 Information Flow Prince William Sound Simulation Quest. I & II Vessel Ops. Questionnaires Quest. III & IV Failure/Error Questionnaires Calibration of Vessel and Situational Relative Incident Probabilities Characterization of PWS Accident Profiles Simulation Weather Data Traffic Data System Description Determination of Relative Incident Probabilities Vessel Reliability & Appropriate Incident Data Quest. V & VI Calibration Questionnaires

17 17 Modeling Traffic Movements Published Data VTS Way Point Data

18 18 Modeling Traffic Movements Nuisance Traffic: Fishing Openers and Regattas Rules of the Road

19 19 Modeling Rules of the Road and Weather (PWS Risk Assessment)

20 20 Continuous vs. Discrete System Time Risk PWS OFI = 5 minutes WSF OFI = 2.5 minutes SFF OFI = 1 minute

21 21 Interacting Vessels

22 22 OFI Counting Opportunity For Incident 2-mile radius 10-mile radius 2-mile radius 10-mile radius

23 23 Ferry Container Vessel 2 Ferries, Parallel Tracks 1 Ferry, 1 Container Vessel Crossing Tracks Scenario 1 Scenario 2 Not Every Interaction is the Same

24 24 OFI Counting Model PASSING (MEETING) CROSSING - - (OVERTAKING) FRONT BACK VesselFerry

25 25 Wind Speed: More than 45 Wind Direction: Perpendicular/on Shore Visibility: Greater than 1/2 mile Bergy Bits within a mileNo Bergy Bits Given a propulsion Failure, Asses the likelihood of Collision (PWS Risk Assessment) Traffic Prox.: Vessels 2 to 10 Miles Traffic Type: Tug with Tow Tanker Size & Direction: Inbound more than 150 DWT Modeling Conditional Failure Probabilities Using Expert Judgment System States

26 26 Expert Judgment Questionnaire (Example: WSF Risk Assessment Vessel Reliability Failure Will Lead to Collision?)

27 27 Example Result

28 28 Accident Probability Model - Regression Paired Comparison 1 2 3 4

29 29 Example Regression Analysis Fit R 2 of Regressions in the order of 75% to 80% Note: This is fit for representing expert data not fit to actual values

30 30 The responses can be enumerated through the use of the exponential risk equation 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

31 31 Simulation Analysis Tool (PWS Risk Assessment)

32 32 Simulation Analysis Tool (WSF Risk Assessment)

33 33 Simulation Analysis Tool (SFF Analysis)

34 Prince William Sound Mitigation Analysis

35 35 Risk Reduction Cases Analyzed

36 36 Displaying Results

37 37 Interactions by Route and Interacting Vessel Washington State Ferry Analysis NON - WSF WSF

38 38 Average Collision Probability per Interaction by Route and Interacting Vessel (WSF) NON - WSF WSF

39 39 Statistical Expected Number of Collisions per Year by Route and Interacting Vessel (WSF) NON - WSF WSF

40 40 Traffic Density Maps San Francisco Bay Analysis

41 41 Traffic Density Comparisons San Francisco Bay Analysis SFB Alternative 3 SFB Alternative 1

42

43 43 Risk Mitigation Effectiveness - PWS

44 44 Risk Mitigation Effectiveness - WSF

45 45 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


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