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 WangEmail author
  • Dan Cox
  • Lori A. Cramer
  • Shangjia Dong
Original Paper


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.


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



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.


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Alireza Mostafizi
    • 1
  • Haizhong Wang
    • 1
    Email author
  • 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

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