Advertisement

Natural Hazards

, Volume 88, Issue 3, pp 1347–1372 | Cite as

Agent-based tsunami evacuation modeling of unplanned network disruptions for evidence-driven resource allocation and retrofitting strategies

  • Alireza Mostafizi
  • Haizhong Wang
  • Dan Cox
  • Lori A. Cramer
  • Shangjia Dong
Original Paper

Abstract

The M9 Cascadia subduction zone earthquake represents one of the most pressing natural hazard threats in the Pacific Northwest of the USA with an astonishing high 7–12% chance of occurrence by 2060, mirroring the 2011 devastating earthquake and tsunami in Japan. Yet this region, like many other coastal communities, is underprepared, lacking a comprehensive understanding of unplanned network disruptions as a key component to disaster management planning and infrastructure resilience. The goals of this paper are twofold: (1) to conduct a network vulnerability assessment to systematically characterize the importance of each link’s contribution to the overall network resilience, with specific emphasis on identifying the most critical set of links and (2) to create an evidence-driven retrofitting resource allocation framework by quantifying the impacts of unplanned network disruptions to the critical links on network resilience and retrofitting planning. This research used the city of Seaside on the Oregon coast as a study site to create the agent-based tsunami evacuation modeling and simulation platform with an explicit focus on the transportation network. The results indicated that (1) the network bridges are not equally important and some of the critical links are counterintuitive and (2) the diverse ways of spending the limited retrofitting resources can generate dramatically different life safety outcomes. These results strongly suggest that accurate characterization and measurement of infrastructure network failures will provide evidence-driven retrofitting planning strategies and inform resource allocations that enhance network resilience.

Keywords

Unplanned network disruption Agent-based tsunami evacuation modeling Evidence-driven resource allocation Retrofitting planning Community resilience 

Notes

Acknowledgements

The authors would like to acknowledge the funding support from the National Science Foundation through grant CMMI #1563618: “An Integrated Social Science and Agent-based Modeling Approach to Improve Life Safety from Near-field Tsunami Hazards” and Oregon Sea Grant program (#NA140AR4170064) through the project “Building resilient coastal communities: A social assessment of mobile technology for tsunami evacuation planning.” Any opinions, findings, and conclusion or recommendations expressed in this research are those of the authors and do not necessarily reflect the view of the funding agencies.

References

  1. Akiyama M, Frangopol DM, Arai M, Koshimura S (2012) Probabilistic assessment of structural performance of bridges under tsunami hazard. ASCE Struct Congr 1919–1928Google Scholar
  2. Berdica K (2002) An introduction to road vulnerability: what has been done, is done and should be done. Transp Polic 9(2):117–127CrossRefGoogle Scholar
  3. Bocchini P, Frangopol DM (2010) Optimal resilience-and cost-based postdisaster intervention prioritization for bridges along a highway segment. J Bridg Eng 17(1):117–129CrossRefGoogle Scholar
  4. Brackstone M, McDonald M (1999) Car-following: a historical review. Transp Res F Traffic Psychol Behav 2(4):181–196CrossRefGoogle Scholar
  5. Chandler RE, Herman R, Montroll EW (1958) Traffic dynamics: studies in car following. Op Res 6(2):165–184CrossRefGoogle Scholar
  6. Chang L, Peng F, Ouyang Y, Elnashai AS, Spencer BF Jr (2012) Bridge seismic retrofit program planning to maximize postearthquake transportation network capacity. J Infrastruct Syst 18(2):75–88CrossRefGoogle Scholar
  7. Earnest DC (2011) Geographic distribution of disruptions in weighted complex networks: an agent-based model of the us air transportation network. Complex adaptive systems. In: AAAI fall symposium, pp 34–43Google Scholar
  8. FEMA (2008) Guidelines for design of structures for vertical evacuation from tsunamis, Technical report, APPLIED TECHNOLOGY COUNCIL, 201 Redwood Shores Pkwy, Suite 240 Redwood City. California 94065Google Scholar
  9. Fessel A, Oettmeier C, Döbereiner H.-G (2014) An analytical model for percolation in small link degree transportation networks. In: Proceedings of the 8th international conference on bioinspired information and communications technologies, pp 81–86Google Scholar
  10. Gazis DC, Herman R, Rothery RW (1961) Nonlinear follow-the-leader models of traffic flow. Op Res 9(4):545–567CrossRefGoogle Scholar
  11. Goldfinger C, Nelson CH, Morey AE, Johnson JE, Patton JR, e.a. Karabanov E (2012) Turbidite event history—methods and implications for holocene paleoseismicity of the cascadia subduction zone. U.S. Geological Survey Professional Paper 1661F, 170Google Scholar
  12. Gonzalez FI, Geist EL, Jaffe B, Kanoglu U, Mofjeld HO, Synolakis C, Titov VV, Arcas DR, Bellomo D, Carlton D, Horning T, Johnson J, Newman J, Parsons T, Peters R, Peterson CD, Priest G, Venturato A, Weber J, Wong FL, Yalciner A (2009) Probabilistic tsunami hazard assessment at seaside, oregon, for near- and far-field seismic sources. J Geophys Res. doi: 10.1029/2008JC005132
  13. He X, Liu H (2012) Modeling the day-to-day traffic evolution process after an unexpected network disruption. Transp Res B 46(1):50–71CrossRefGoogle Scholar
  14. Herman R, Montroll EW, Potts RB, Rothery RW (1959) Traffic dynamics: analysis of stability in car following. Op Res 7(1):86–106CrossRefGoogle Scholar
  15. Jenelius E, Mattsson LG (2012) Road network vulnerability analysis of area-covering disruptions: a grid-based approach with case study. Transp Res A Polic Pract 46(5):746–760CrossRefGoogle Scholar
  16. Jenelius E, Petersen T, Mattsson LG (2006) Importance and exposure in road network vulnerability analysis. Transp Res A Pol Pract 40(7):537–560CrossRefGoogle Scholar
  17. Jenelius E, Mattsson L.-G, Levinson D (2010) The traveler costs of unplanned transport network disruptions: an activity-based modeling approach. 1–34Google Scholar
  18. Jonkman SN, Vrijling JK, Vrouwenvelder ACWM (2008) Methods for the estimation of loss of life due to floods: a literature review and a proposal for a new method. Nat Hazards 46:353–389CrossRefGoogle Scholar
  19. Kiremidjian A, Moore J, Fan YY, Yazlali O, Basoz N, Williams M (2007) Seismic risk assessment of transportation network systems. J Earthq Eng 11(3):371–382CrossRefGoogle Scholar
  20. Klugl F, Bazzan ALC (2012) Agent-based modeling and simulation. AI Mag 29–40Google Scholar
  21. Knoblauch R, Pietrucha M, Nitzburg M (1995) Field studies of pedestrian walking speed and start-up time. Transp Res Rec 1538:27–38CrossRefGoogle Scholar
  22. Konduri KC, Pendyala RM, You D, Chiu Y.-C, Hickman M, Noh H, Gardner B, Waddell P, Wang L (2013) A network-sensitive transport modeling framework for evaluating impacts of network disruptions on traveler choices under varying levels of user information provision. In: 92nd Annual meeting of the Transportation Research Board, Washington, DC, p 510Google Scholar
  23. Koshimura S, Katada T, Mofjeld HO, Kawata Y (2006) A method for estimating casualties due to the tsunami inundation flow. Nat Hazards 39(2):265–274CrossRefGoogle Scholar
  24. Lind N, Hartford D, Assaf H (2004) Hydrodynamic models of human stability in a flood. JAWRA J Am Water Resour As 40(1):89–96. doi: 10.1111/j.1752-1688.2004.tb01012.x CrossRefGoogle Scholar
  25. Lindell MK, Prater CS (2007) Critical behavioral assumptions in evacuation time estimate analysis for private vehicles: examples from hurricane research and planning. J Urban Plan Devel 133(1):18–29CrossRefGoogle Scholar
  26. Mas E, Imamura F, Koshimura S (2011) Modeling the decision of evacuation from tsunami, based on human risk perception. In: Annual meeting of the Tohoku Branch Technology Research conferenceGoogle Scholar
  27. Mas E, Suppasri A, Imamura F, Koshimura S (2012) Agent-based simulation of the 2011 great east Japan earthquake/tsunami evacuation: an integrated model of tsunami inundation and evacuation. J Nat Disaster Sci 34(1):41–57CrossRefGoogle Scholar
  28. Mas Erick FI, Koshimura S (2012) An agent-based model for the tsunami simulation: case study of the 2011 great east Japan tsunami in arahama town. Joint conference proceeding. In: 9th International conference on urban earthquake engineering & 4th Asia conference on earthquake engineering. Tokyo Institute of Technology, Tokyo, JapanGoogle Scholar
  29. Miller HJ, Wu Y.-H, Hung M.-C (1999) Gis-based dynamic traffic congestion modeling to support time-critical logistics. In: Proceedings of the 32nd annual Hawaii international conference on systems sciences. HICSS-32Google Scholar
  30. Mostafizi A (2016) Agent-based tsunami evacuation model : life safety and network resilience, Master’s thesis, Oregon State UniversityGoogle Scholar
  31. Muhari A, Imamura F, Koshimura S, Post J (2011) Examination of three practical run-up models for assessing tsunami impact on highly populated areas. Nat Hazards Earth Syst Sci 11(12):3107–3123CrossRefGoogle Scholar
  32. Murray-Tuite P (2007) Perspectives for network management in response to unplanned disruptions. J Urban Plan Dev 133(1):9–17CrossRefGoogle Scholar
  33. Murray-Tuite P, Mahmassani HS (2005) Identification of vulnerable transportation infrastructure and household decision making under emergency evacuation conditions. No. SWUTC/05/167528-1, Southwest Region University Transportation Center, Center for Transportation Research, University of Texas at AustinGoogle Scholar
  34. Naser M, Birst SC (2010) Mesoscopic evacuation modeling for small- to medium-sized metropolitan areas, Technical report, Advanced Traffic Analysis Center, Upper Great Plains Transportation Institute, North Dakota State University, Fargo, ND 58108Google Scholar
  35. Pan X, Han CS, Dauber K, Law KH (2007) A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. AI Soc 22:113–132CrossRefGoogle Scholar
  36. Park H, Cox DT (2016) Probabilistic assessment of near-field tsunami hazards: inundation depth, velocity, momentum ux, arrival time, and duration applied to seaside, oregon. Coast Eng 117:79–96CrossRefGoogle Scholar
  37. Park S, van de Lindt JW, Gupta R, Cox D (2012) Method to determine the locations of tsunami vertical evacuation shelters. Nat Hazards 63(2):891–908CrossRefGoogle Scholar
  38. Priest GR, Stimely LL, Wood NJ, Madin IP, Watzig RJ (2016) Beat-the-wave evacuation mapping for tsunami hazards in seaside, Oregon, USA. Nat Hazards 80(2):1031–1056CrossRefGoogle Scholar
  39. Priest GR, Witter RC, Zhang YJ (2013) Tsunami animations, time histories, and digital point data for flow depth, elevation, and velocity for the South Coast Project Area, Curry County, Oregon, Technical Report Open-File Report O-13-13, Department of Geology and Mineral IndustriesGoogle Scholar
  40. Qian ZS, Zhang HM (2013) Full closure or partial closure? Evaluation of construction plans for the i-5 closure in downtown Sacramento. J Transp Eng 139(3):273–286CrossRefGoogle Scholar
  41. Rahimian S, Mcneil S (2012) Post earthquake transportation network performance: transportation of injured to medical facilities. In: 2012 NZSEE conference, p 59Google Scholar
  42. Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. Simulation 82(9):609–623CrossRefGoogle Scholar
  43. Satake K, Wang K, Atwater BF (2003) Fault slip and seismic moment of the 1700 Cascadia earthquake inferred from Japanese tsunami descriptions. J Geophys Res 108(B11):1978–2012CrossRefGoogle Scholar
  44. Schulz K (2015a) How to stay safe when the big one comes. http://www.newyorker.com/tech/elements/how-to-stay-safe-when-the-big-one-comes
  45. Schulz K (2015b) The really big one, Annals of Seismology. http://www.newyorker.com/magazine/2015/07/20/the-really-big-one
  46. Scott DM, Novak DC, Aultman-Hall L, Guo F (2006) Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. J Transp Geogr 14(3):215–227CrossRefGoogle Scholar
  47. Sullivan JL, Aultman-Hall L, Novak DC (2009) A review of current practice in network disruption analysis and an assessment of the ability to account for isolating links in transportation networks. Transp Lett 1(4):271–280CrossRefGoogle Scholar
  48. Sullivan JL, Novak DC, Aultman-Hall L, Scott DM (2010) Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach. Transp Res A Polic Pract 44(5):323–336CrossRefGoogle Scholar
  49. Tatano H, Tsuchiya S (2008) A framework for economic loss estimation due to seismic transportation network disruption: a spatial computable general equilibrium approach. Nat Hazards 44(2):253–265CrossRefGoogle Scholar
  50. Thiele JC (2014) R marries NetLogo: introduction to the RNetLogo package. J Stat Softw 58(2):1–41CrossRefGoogle Scholar
  51. Thiele JC, Kurth W, Grimm V (2012) RNetLogo: an R package for running and exploring individual-based models implemented in NetLogo. Methods Ecol Evol 3(3):480–483CrossRefGoogle Scholar
  52. Titov VV, Gonzalez FI (1997) Implementation and testing of the method of splitting tsunami (MOST) model, Technical report, US Department of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Pacific Marine Environmental LaboratoryGoogle Scholar
  53. TRB, Highway Capacity Manual, (2010) Transportation Research Board. National Research Council, Washington, D.C., p 2010Google Scholar
  54. Tweedie SW, Rowland JR, Walsh SJ, Rhoten RP (1986) A methodology for estimating emergency evacuation times. Soc Sci J 23(2):189–204CrossRefGoogle Scholar
  55. Venturato AJ, Arcas D, Kanoglu U (2007) Modeling tsunami inundation from a Cascadia subduction zone earthquake for long beach and ocean shores, Washington, Technical report, National Oceanic and Atmospheric Administration, Seattle. WA, Pacific Marine Environmental LabGoogle Scholar
  56. Wang H, Mostafizi A, Cramer LA, Cox D, Park H (2016) An agent-based model of a multimodal near-field tsunami evacuation: decision-making and life safety. Transp Res C Emerg Technol 64:86–100CrossRefGoogle Scholar
  57. Wilensky U (1999) Netlogo. http://ccl.northwestern.edu/netlogo/. Center for connected learning and computer-based modeling, Northwestern University. Evanston, IL
  58. Wood N (2007) Variations in city exposure and sensitivity to tsunami hazards in Oregon., Technical report 5283, U.S. Geological Survey Scientific Investigations ReportGoogle Scholar
  59. Wood NJ, Schmidtlein MC (2012) Anisotropic path modeling to assess pedestrian-evacuation potential from Cascadia-related tsunamis in the US Pacific Northwest. Nat Hazards 62(2):275–300CrossRefGoogle Scholar
  60. Wood NJ, Schmidtlein MC (2013) Community variations in population exposure to near-field tsunami hazards as a function of pedestrian travel time to safety. Nat Hazards 65(3):1603–1628CrossRefGoogle Scholar
  61. Wood NJ, Jones J, Spielman S, Schmidtlein MC (2016) Community clusters of tsunami vulnerability in the us pacific northwest. Proc Nat Acad Sci 112(17):5354–5359CrossRefGoogle Scholar
  62. Wood N, Jones J, Schelling J, Schmidtlein M (2014) Tsunami vertical-evacuation planning in the US Pacific Northwest as a geospatial, multi-criteria decision problem. Int J Disaster Risk Reduct 9:68–83CrossRefGoogle Scholar
  63. Xie F, Levinson D (2011) Evaluating the effects of the i-35w bridge collapse on road-users in the twin cities metropolitan region. Transp Plan Technol 34(7):691–703CrossRefGoogle Scholar
  64. Yeh H (2010) Gender and age factors in tsunami casualties. Nat Hazards Rev 11(1):29–34CrossRefGoogle Scholar
  65. Yin W, Murray-Tuite P, Ukkusuri SV, Gladwin H (2014) An agent-based modeling system for travel demand simulation for hurricane evacuation. Transp Res C Emerg Technol 42:44–59CrossRefGoogle Scholar
  66. Zhu S, Levinson DM (2012) Disruptions to transportation networks: a review. Network reliability in practice. Springer, New YorkGoogle Scholar
  67. Zhu S, Levinson D, Liu HX, Harder K (2010) The traffic and behavioral effects of the i-35w Mississippi river bridge collapse. Transp Res A Polic Pract 44(10):771–784CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Alireza Mostafizi
    • 1
  • Haizhong Wang
    • 1
  • Dan Cox
    • 1
  • Lori A. Cramer
    • 2
  • Shangjia Dong
    • 1
  1. 1.School of Civil and Construction EngineeringOregon State UniversityCorvallisUSA
  2. 2.School of Public PolicyOregon State UniversityCorvallisUSA

Personalised recommendations