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Modeling the Interaction between Emergency Communications and Behavior in the Aftermath of a Disaster

  • Shridhar Chandan
  • Sudip Saha
  • Chris Barrett
  • Stephen Eubank
  • Achla Marathe
  • Madhav Marathe
  • Samarth Swarup
  • Anil Kumar S. Vullikanti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7812)

Abstract

We describe results from a computer simulation-based study of a large-scale, human-initiated crisis in a densely populated urban setting. We focus on the interaction between human behavior and the communication infrastructure in the aftermath of the crisis. We study the effects of sending emergency broadcasts immediately after the event, advising people to shelter in place, and show that this relatively mild intervention can have a large beneficial impact.

Keywords

synthetic information computer simulations disaster modeling nuclear terrorism 

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References

  1. 1.
    Buddemeier, B.R., Valentine, J.E., Millage, K.K., Brandt, L.D.: National Capital Region: Key response planning factors for the aftermath of nuclear terrorism. Technical Report LLNL-TR-512111, Lawrence Livermore National Lab. (2011)Google Scholar
  2. 2.
    Wein, L.M., Choi, Y., Denuit, S.: Analyzing evacuation versus shelter-in-place strategies after a terrorist nuclear detonation. Risk Analysis 30(6) (2010)Google Scholar
  3. 3.
    Dombroski, M.J., Fischbeck, P.S.: An integrated physical dispersion and behavioral response model for risk assessment of radiological dispersion device (RDD) events. Risk Analysis 26(2), 501–514 (2006)CrossRefGoogle Scholar
  4. 4.
    Homeland Security Council Interagency Policy Coordination Subcommittee: Planning guidance for response to a nuclear detonation (2009)Google Scholar
  5. 5.
    Millage, K.: Modeling the effects of nuclear weapons in an urban setting. In: Radiation Countermeasures Symposium an AFRRI 50th Anniversary Event (2011)Google Scholar
  6. 6.
    Panzieri, S., Setola, R.: Failure propagation in critical interdependent infrastructures. Int. J. Modeling, Identification and Control, 69–78 (2008)Google Scholar
  7. 7.
    Peeta, S., Hsu, Y.T.: Integrating Supply and Demand Aspects of Transportation for Mass Evacuation under Disasters. Technical Report NEXTRANS Project No. 019PY01, Purdue University (October 2010)Google Scholar
  8. 8.
    Barrett, C., Beckman, R., Channakeshava, K., Huang, F., Kumar, V.A., Marathe, A., Marathe, M.V., Pei, G.: Cascading failures in multiple infrastructures: From transportation to communication network. In: The Fifth International IEEE CRIS Conference on Critical Infrastructures, Beijing, China (2010)Google Scholar
  9. 9.
    Little, R.: Controlling cascading failure: Understanding the vulnerabilities of interconnected infrastructures. Journal of Urban Technology, 109–123 (2002)Google Scholar
  10. 10.
    Barrett, C., Beckman, R., Channakeshava, K., Huang, F., Kumar, V.A., Marathe, A., Marathe, M.V., Pei, G., Saha, S.: Human initiated cascading failures in societal infrastructures. PLoS ONE (2012)Google Scholar
  11. 11.
    Liu, H.X., Ban, J.X., Ma, W., Mirchandani, P.B.: Model reference adaptive control framework for real-time traffic management under emergency evacuation. Journal of Urban Planning and Development 133(1), 43–50 (2007)CrossRefGoogle Scholar
  12. 12.
    Drabek, T.E., Boggs, K.S.: Families in disaster: reactions and relatives. Journal of Marriage and the Family 30, 443–451 (1968)CrossRefGoogle Scholar
  13. 13.
    Gerber, B.J., Ducatman, A., Fischer, M., Althouse, R., Scotti, J.R.: The potential for an uncontrolled mass evacuation of the DC metro area following a terrorist attack: A report of survey findings. Technical report, West Virginia University, Homeland Security Programs (2006)Google Scholar
  14. 14.
    Guterbock, T.M., Lambert, J.H., Bebel, R.A., Parker, M.W.: NCR behavioral survey 2011: Work, school or home? Issues in sheltering in place during an emergency. Technical report, Center for Survey Research, University of Virginia (August 2011)Google Scholar
  15. 15.
    NDSSL, Virginia Tech: Computational analysis of behavior and urban disaster resilience, http://ndssl.vbi.vt.edu/projects/disaster-resilience/
  16. 16.
    Beckman, R., Channakeshava, K., Huang, F., Kumar, V., Marathe, A., Marathe, M., Pei, G.: Synthesis and analysis of spatio-temporal spectrum demand patterns: A first principles approach. In: IEEE DySPAN (2010)Google Scholar
  17. 17.
    TowerMaps: Wireless antenna facility location data, http://www.towermaps.com/
  18. 18.
    Center for Disease Control: National Health Interview Survey (NHIS), http://www.cdc.gov/nchs/about/major/nhis/nhis_2007_data_release.htm
  19. 19.
    Beckman, R., Channakeshava, K., Huang, F., Kumar, V.A., Marathe, A., Marathe, M., Pei, G.: Implications of dynamic spectrum access on the efficiency of primary wireless market. IEEE Dynamic Spectrum Access Networks, DySPAN, 2–12 (April 2010)Google Scholar
  20. 20.
    Kim, J., Kumar, V., Marathe, A., Pei, G., Saha, S., Subbiah, B.: Modeling cellular network traffic with mobile call graph constraints. In: Proceedings of the 2011 Winter Simulation Conference, WSC, pp. 3165–3177 (December 2011)Google Scholar
  21. 21.
    Saha, S., Kumar, V., Marathe, A., Pei, G., Subbiah, B., Kim, J.: Clearing secondary spectrum market with spatio-temporal partitioning. IEEE Dynamic Spectrum Access Networks, DySPAN (October 2012)Google Scholar
  22. 22.
    Kim, J., Kumar, V., Marathe, A., Pei, G., Saha, S., Subbiah, B.: Analysis of policy instruments for enhanced competition in spectrum auction. IEEE Dynamic Spectrum Access Networks, DySPAN (October 2012)Google Scholar
  23. 23.
    Parikh, N., Swarup, S., Stretz, P., Rivers, C., Lewis, B., Marathe, M., Eubank, S., Barrett, C., Lum, K., Chungbaek, Y.: Modeling human behavior in the aftermath of a hypothetical improvised nuclear detonation. In: The 12th International Conference on Autonomous Agents and Multiagent Systems, AAMAS, Minnesota, USA (May 2013)Google Scholar
  24. 24.
    Lewis, B., Swarup, S., Bisset, K., Eubank, S., Marathe, M., Barrett, B.: A Simulation Environment for the Dynamic Evaluation of Disaster Preparedness Policies (2013)Google Scholar
  25. 25.
    Barrett, C., Bisset, K., Leidig, J., Marathe, A., Marathe, M.: An integrated modeling environment to study the co-evolution of networks, individual behavior, and epidemics. AI Magazine 31(1), 75–87 (2010)Google Scholar
  26. 26.
    Beckman, R.J., Baggerly, K.A., McKay, M.D.: Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice 30(6), 415–429 (1996)CrossRefGoogle Scholar
  27. 27.
    Network Dynamics and Simulation Science Laboratory: Social, Health and Socio-technical effects of an IND in the National Capitol (2012)Google Scholar
  28. 28.
    Sutton, R., Precup, D., Singh, S.: Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112(1-2), 181–211 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Barrett, C., Eubank, S., Marathe, A., Marathe, M., Pan, Z., Swarup, S.: Information integration to support policy informatics. The Innovation Journal 16(1), article 2 (2011)Google Scholar
  30. 30.
    Barrett, C., Beckman, R., Berkbigler, K., Bisset, K., Bush, B., Campbell, K., Eubank, S., Henson, K., Hurford, J., Kubicek, D., Marathe, M., Romero, P., Smith, J., Smith, L., Speckman, P., Stretz, P., Thayer, G., Eeckhout, E., Williams, M.D.: TRANSIMS: Transportation analysis and simulation system. Technical Report LA-UR-00-1725, Los Alamos National Laboratory (2001)Google Scholar
  31. 31.
    Barrett, C., Beckman, D., Khan, M., Kumar, V.A., Marathe, M., Stretz, P., Dutta, T., Lewis, B.: Generation and analysis of large synthetic social contact networks. In: Winter Simulation Conference (2009)Google Scholar
  32. 32.
    Beckman, R., Channakeshava, K., Huang, F., Marathe, A., Marathe, M., Pei, G., Saha, S., Vullikanti, A.: Integrated multi-network modeling environment for spectrum management. NDSSL Technical Report (2013)Google Scholar
  33. 33.
    Eubank, S.G., Guclu, H., Kumar, V.S.A., Marathe, M.V., Srinivasan, A., Toroczkai, Z., Wang, N.: Modelling disease outbreaks in realistic urban social networks. Nature 4, 180–184 (2004)CrossRefGoogle Scholar
  34. 34.
    Human Behavior and WMD Crisis /Risk Communication Workshop: Defense Threat Reduction Agency, Federal Bureau of Investigation, U.S. Joint Forces Command (March 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shridhar Chandan
    • 1
  • Sudip Saha
    • 1
  • Chris Barrett
    • 1
  • Stephen Eubank
    • 1
  • Achla Marathe
    • 1
  • Madhav Marathe
    • 1
  • Samarth Swarup
    • 1
  • Anil Kumar S. Vullikanti
    • 1
  1. 1.Network Dynamics and Simulation Science LaboratoryVirginia Bioinformatics Institute, Virginia TechBlacksburgUSA

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