A SYSTEMS APPROACH TOWARDS RISK INTERVENTION PRIORITIZATION IN MARITIME ENVIRONMENT

1. Engineering Management & Systems Engineering Department 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)

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 AssessmentWashington State Department of Transportation The George Washington University Rensselaer Polytechnic Institute Virginia Commonwealth University

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

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. 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 Situational Risk Factors • 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 Low Performing Risk Prone Organizations 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. Stage 6 Delayed Consequence Stage 5 Consequence Stage 1 Basic/Root Causes Stage 2 Immediate Causes E.g. Environmental Damage, Loss of Life E.g. Oil Outflow, Persons in Peril Stage 3 Incident Stage 4 Accident E.g. Inadequate Skills, Knowledge, Equipment, Maintenance, Management E.g. Human Error, Equipment Failure, E.g. Loss of Power, Loss of Steering , Dangerous Navigation E.g. Collisions, Groundings, 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 SITUATIONAL FACTORS Type of waterway Wind Speed Traffic situation Wind Direction Traffic density Current Visibility Time of day The Maritime Accident Event Chain

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

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

15. Vessel Attributes Waterway Attributes Data on technological failures Expert Judgement on Human Error Data + effect of waterway attributes from expert judgment Simulation + Counting Model Modeling the Causal Chain: Collision Risk Opportunity for Incident Incident Collision 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 Quest. V & VI Calibration Questionnaires Vessel Reliability & Appropriate Incident Data Determination of Relative Incident Probabilities Simulation Weather Data Characterization of PWS Accident Profiles Calibration of Vessel and Situational Relative Incident Probabilities Traffic Data System Description

17. Modeling Traffic Movements VTS Way Point Data Published Data

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

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

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

21. Interacting Vessels

22. OFI CountingOpportunity For Incident 10-mile radius 10-mile radius 2-mile radius 2-mile radius

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

24. Vessel Ferry OFI Counting Model FRONT CROSSING BACK PASSING (MEETING) PASSING (OVERTAKING) - CROSSING BACK - FRONT

25. 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 “System States” Wind Direction: Perpendicular/on Shore Wind Speed: More than 45 Visibility: Greater than 1/2 mile No Bergy Bits Bergy Bits within a mile

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

27. Example Result

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

29. 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

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. Simulation Analysis Tool(PWS Risk Assessment)

32. Simulation Analysis Tool(WSF Risk Assessment)

33. Simulation Analysis Tool(SFF Analysis)

34. Prince William Sound Mitigation Analysis

35. Risk Reduction Cases Analyzed

36. Displaying Results

37. NON - WSF WSF Interactions by Route and Interacting VesselWashington State Ferry Analysis

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

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

40. Traffic Density MapsSan Francisco Bay Analysis

41. Traffic Density ComparisonsSan Francisco Bay Analysis SFB Alternative 3 SFB Alternative 1

43. Risk Mitigation Effectiveness - PWS

44. Risk Mitigation Effectiveness - WSF

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