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An Agent-Based Evacuation Model to Improve Safety in the Cascadia Subduction Zone


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Haizhong Wang, Oregon State University

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An Agent-Based Evacuation Model to Improve Safety in the Cascadia Subduction Zone

  1. 1. An Agent-Based Evacuation Model to Improve Life Safety in the Cascadia Subduction Zone Haizhong Wang, Associate Professor, Civil Engineering Oregon State University, Corvallis, OR with contributions from Alireza Mostafizi, Ph.D Student, OSU Dan Cox, Professor, Civil Engineering, OSU Lori Cramer, Associate Professor, Public Policy, OSU TREC Friday Transportation Seminar April 19th, 2019 Portland State University
  2. 2. Research Team Dr. Haizhong Wang, Transportation Agent-based Evacuation Modeling Dr. Daniel Cox, Coastal Engineering Tsunami Inundation Modeling Dr. Michael K. Lindell and Dr. Lori Cramer, Social Science, Evacuee Decision-making Alireza Mostafizi, Ph.D Student Hyoungsu Park, Post-doc Natalie Marshall, URA Julia Waters, URAChen Chen, Ph.D Student
  3. 3. NSF RAPID Reconnaissance Data Collection Trip to Palu, Indonesia, April 8-16th, 2019
  4. 4. Tsunami Evacuation: Minutes matter! An Example 2011 Tohoku Event
  5. 5. Interdisciplinary Disaster Research
  6. 6. Temporal and Spatial Dimension of Disasters
  7. 7. Critical Need for a Decision-Making Tool Couples: • Earthquake Damages and Tsunami Inundation Physics • Multi-modal Transportation (On foot vs. Driving) • Human Decision Making (i.e., Milling time) To inform: • Tsunami Evacuation Planning • Resource Allocation and Retrofitting Decisions • Tsunami Education and Outreach Efforts
  8. 8. Methodology: Agent-based Models and Monte Carlo Simulation
  9. 9. What is an agent? µ average of agent maximum speed [m/s] σ SD of agent maximum speed [m/s] R visibility radius [m] D dimension of forward domain [m] (domain: Dx2D) m speed reduction rate in passing θ modification of moving angle in passing [deg] P probability of making forced passing class diagram parameters Agent Ability Thought See(): Think(): Move(): Ability MaximumSpeed Visibility PassingProbability Direction Speed Path Thought see move think agent see move think agent Credit: Prof. Hori Mueno at Tokyo University
  10. 10. Formulation: Agent Dynamics • Dynamic problem of agent position 𝑑𝑑 𝑑𝑑𝑑𝑑 𝒙𝒙𝛼𝛼 𝑡𝑡 = 𝒗𝒗(𝐴𝐴𝛼𝛼, 𝐸𝐸 𝛼𝛼; 𝑃𝑃𝛼𝛼) 𝒙𝒙𝛼𝛼 a-th agent position (a function of time) 𝐴𝐴𝛼𝛼 set of agents viewed by a-th agent 𝐸𝐸 𝛼𝛼 set of environment items viewed by a-th agent 𝑃𝑃𝛼𝛼 characteristics of a-th agent Credit: Prof. Hori Mueno at Tokyo University Source: Kardi and Millonig (2007), A Navigation Algorithm for Pedestrian Simulation in Dynamic Environments, WCTR.
  11. 11. Social Force Model (Dirk Helbing, 2000) Source: Dirk Helbing et. al., (2000) Simulating Dynamical Features of Escape Panic, Nature: Letters to Nature.
  12. 12. ABTEM: Agent-based Tsunami Evacuation Models
  13. 13. Walking Speeds
  14. 14. Evacuation Delay Time Delayed start Tweedie et al. (1986) Sorensen (2000) Lindell and Prater (2007) tau 95% Time in minutes required for 95% of agents to take some action with tau = 5 min 7.4 (sig =1) 9.9 (sig=2) 14.8 (sig=4) 24.6 (sig=8) Milling Time
  15. 15. B Agents do not cross river east to west (toward hazard) N W E S Options 0, 1, 2, 3 are ok B Options 0, 1, 2 are ok can not do Opt 3 (VE) X ABM  Can implement “simple rules” to see complex behavior patterns emerge
  16. 16. Empirical Validation: Experiential Evacuation Drills 8/10 (Light Blue (NEW)) — HMSC-REU/OMSI After Dark 2 7/13 (Violet) — HMSC-REU After Dark 1 6/26 (Turquoise) — CCE SURF students 6/16 (Brown) — HMSC evacuation 5/11 (Orange) — OPRD, SBSP 2/18 (Magenta) — First try ABM (Yellow Triangle)
  17. 17. Data Collection: Use a Smartphone App to Track Individuals in Drills
  18. 18. Using Social Behavior Data to Validate the ABM
  19. 19. 7/13 Tsunami After Dark participants and event leaders
  20. 20. Category Census data Seaside Total Sample Total population (18 and over) 5,163 211 Persons 65 years and older (percent of total population over 18 years) 21.8% 45.7% Owner occupied housing units (percent of total occupied units) 44.4% 63.8% Female (18 and over) 52% 56.7% Average household size 2.21 2.0 Bachelor's degree or higher (population over 25, 2012-2016) 20.9% 43.4% Median household income (in 2016 dollars), 2012-2016 $36,373 $40,000-49,000 Seaside Tsunami Evacuation Expectations Survey
  21. 21. Q8 . Do you agree or disagree that people could increase their chances of surviving a M9 Cascadia earthquake and tsunami by making various emergency plans or preparatory actions? (Circle one number) 1. Strongly disagree 2. Disagree 3. Neither 4. Agree 5. Strongly agree 3.79% 4.27% 6.64% 42.65% 40.28% 2.37% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% Strongly Disagree Disagree Neither Agree Strongly agree Missing
  22. 22. Q19. What is your assessment of the general level of community preparedness for Cascadia earthquakes and tsunamis in your local area? (Circle one answer) 1. Lacking 2. Poor 3. Adequate 4. Good 5. Excellent I don’t know 12% 34% 19% 20% 6% 9% 0% 5% 10% 15% 20% 25% 30% 35% 40% Lacking Poor Adequate Good Excellent I don't know Percentage
  23. 23. Application 1: Multimodal Evacuation
  24. 24. Mode Split in Multimodal Evacuation
  25. 25. Application 2:Creation of a Multitouch Tsunami Exhibit at HMSC-VC
  26. 26. Tsunami Interactive Multi-touch Exhibit (TIME)
  27. 27. Here are what the evacuation models look like in the middle of the video. To see the whole video go to:
  28. 28. An Adult and Child watching the Rocks Route. Important notes: Mentions Speed Decides Best Route Preparation
  29. 29. Application 3: Vertical Evacuation Behavior and Shelter Locations
  30. 30. Middle of the Beach South of the beach Vertical Evacuation Sheltering
  31. 31. Application 4: Critical Links Identification
  32. 32. Unplanned Network Disruption – Seaside Broadway BridgeU bridge
  33. 33. Network Disruption I – U Bridge No Failed Link U Bridge Failed
  34. 34. Network Disruption II - Broadway Bridge No Failed Link Broadway Bridge Failed
  35. 35. Critical Links Criteria
  36. 36. Engineering Implications to ODOT: Retrofitting Decisions
  37. 37. Major Takeaways Today 1. For Cascadia Subduction Zone Earthquake and Tsunami, it is NOT about If it will happen or not, it is about when it happen. 2. Improve Tsunami Hazard Literacy Education on the coast, i.e., do people recognize ground shaking as a natural cue for potential tsunami evacuation on the coast? 3. Preparing for the Really Big One requires multidisciplinary expertise and deep collaborations among individual/households, community leaders, emergency managers, local/state government, industry etc. 4. We can not prevent it, what we can do is to prepare people and community to be ready for it.
  38. 38. Representative Publications and Presentations: A. Mostafizi, H. Wang, D. Cox, S. Dong. “An agent-based Vertical Evacuation Model for a near-field tsunami: Choice behavior, logical shelter locations, and life safety”. International Journal of Disaster Risk Reduction, March 2019, Vol. 34, pp. 467-479. A. Mostafizi, H. Wang, and Shangjia Dong. "Understanding the Multimodal Evacuation Behavior for a Near-field Tsunami". Transportation Research Record: Journal of the Transportation Research Board, Feb. 2019. A. Mostafizi, H. Wang, D. Cox, L. Cramer. “Agent-Based Tsunami Evacuation Modeling with Unplanned Network Disruptions for Evidence-driven Resource Allocation and Retrofitting Strategies”. Natural Hazards, September 2017, Volume 88, Issue 3, pp 1347–1372. H. Wang, A. Mostafizi, L. A. Cramer, D. Cox, and H. Park. “An Agent-based Modeling of a Multimodal Near-field Tsunami Evacuation Decision-Making and Life Safety”. Transportation Research Part C: Emerging Technologies, Volume 64, Pages 86-100, 2016. A. Mostafizi, H. Wang, S. Dong, and D. Cox. “An Agent-based Model of Vertical Tsunami Evacuation Behavior and Shelter Locations: A Multi-Criteria Decision-Making Problem”. Transportation Research Board Annual Meeting, Washington D. C., Jan. 2018.
  39. 39. Acknowledgements Mr. Brian Fowler, Oregon Department of Parks and Recreation Mr. Mark Farley, Hatfield Marine Science Center, Newport, Oregon Dr. Michael K. Lindell, Affiliate Professor, University of Washington Seattle Dr. Xuan Song, Associate Professor, Tokyo University, Japan Dr. Rahmawati Husein, Assistant Professor, UMY, Indonesia Dr. Sudirman, University Alkhairaat Palu (Palu UNISA), Indonesia
  40. 40. Acknowledgement The authors would like to acknowledge the funding support from Oregon Sea Grant and National Science Foundation (NSF). Acknowledgement This material is based upon work funded by the National Science Foundation under grant #1563618, #1826455, #1902888 and Oregon Sea Grant #NA140AR4170064. Haizhong Wang, Thank you!