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Agent-Based Modeling and Evacuation Planning

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Geospatial Technologies and Homeland Security

Part of the book series: The GeoJournal Library ((GEJL,volume 94))

Abstract

In evacuation planning, it is advantageous for community leaders to have a thorough understanding of the human and geophysical characteristics of a community, be able to anticipate possible outcomes of different response and evacuation strategies under different situations, inform the general public, and develop a set of evacuation plans accordingly. In order to achieve this goal, evacuation managers in a community can use computer modeling techniques to simulate different ‘what-if’ scenarios, use the results from these simulations to inform the public, and generate different evacuation plans under different circumstances. The complexity associated with evacuation planning in an urban environment requires a computer modeling framework that can incorporate a number of factors into the modeling process. These factors include the nature of the disaster in question, the anticipated human behavioral patterns in the evacuation process, the unique geography and transportation infrastructure in a given area, the population distribution in the area, the population dynamics over different time periods, and the special needs of different population groups, to name a few. Agent-Based Modeling (ABM) provides a general approach that can be used to account for these factors in the modeling and simulation process. In this chapter, the authors provide an overview of agent-based modeling and simulation, illustrate how agent-based modeling and simulation were used in estimating the evacuation time for the Florida Keys, and report some preliminary results in planning a hypothetical route for evacuating the elderly from a nursing home on Galveston Island, Texas, based on network dynamics during an evacuation.

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References

  • An Applied Technology and Traffic Analysis Program (ATTAP) (2006). A real-time emergency evacuation system for the Washington, D.C. metropolitan area [Electronic version]. University of Maryland. Retrieved from http://attap.umd.edu/bbs/zboard.php?id = highlights.

  • Baker, E. J. (2000). Hurricane evacuation behavioral assumptions for the Florida Keys. Department of Geography, Florida State University, Tallahassee, FL.

    Google Scholar 

  • Batty, M., DeSyllas, J. & Duxbury, E. (2003). The discrete dynamics of small-scale spatial events: Agent-based models of mobility in carnivals and streetpParades. International Journal of Geographical Information Science, 17, 673–697.

    Article  Google Scholar 

  • Bonabeau, E. (2002). Agent-based modelling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America, 99, 7280–7287.

    Article  Google Scholar 

  • Chen, M. (2005). Traffic signal timing for urban evacuation. Master thesis, University of Maryland–College Park.

    Google Scholar 

  • Chen, X., Meaker J. & Zhan, F. B. (2006). Agent-based modelling and analysis of hurricane evacuation procedures for the Florida Keys. Natural Hazards, 38, 321–338.

    Article  Google Scholar 

  • Chen, X. & Zhan, F. B. (2006). Agent-based modelling and simulation of urban evacuation: Relative effectiveness of simultaneous and staged evacuation strategies. Journal of the Operational Research Society, doi: 10.1057/palgrave.jors.2602321).

    Google Scholar 

  • Chiu, Y. (2004). Traffic scheduling simulation and assignment for area-wide evacuation. (In Proceedings of the 2004 IEEE Intelligent Transportation Systems Conference (pp. 537–542) ).

    Google Scholar 

  • Church, R. L. & Sexton, R. M. (2002). Modelling small area evacuation: Can existing transportation infrastructure impede public safety? Vehicle Intelligence & Transportation Analysis Laboratory, University of California, Santa Barbara, CA.

    Google Scholar 

  • Cova, T. J. & Johnson, J. P. (2002). Microsimulation of neighborhood evacuations in the urban-wildland interface. Environment and Planning A, 34, 2211–2229.

    Article  Google Scholar 

  • Cova, T. J. & Johnson, J. P. (2003). A network flow model for lane-based evacuation routing. Transportation Research Part A–Policy and Practice, 37, 579–604.

    Article  Google Scholar 

  • Cutter, S. (2006). The geography of social vulnerability: Race, class, and catastrophe [Electronic version]. Retrieved September 25, 2007 from http://understandingkatrina.ssrc.org/Cutter/.

  • Farahmand, K. (1997). Application of simulation modelling to emergency population evacuation. (In S. Andradcittir, K. J. Healy, D. H. Withers, & B. L. Nelson (Eds.), Proceedings of the 1997 winter simulation conference (pp. 1181–1188) ).

    Google Scholar 

  • Federal Highway Administration (FHWA) (2005) Corridor simulation (CORSIM/TSIS) [Electronic version]. Retrieved December 20, 2005, from http://ops.fhwa.dot.gov/trafficanalysistools/ corsim.htm.

  • Franzese, O. & Han, L. (2001). Traffic modelling framework for hurricane evacuation. (In Proceedings of the Transportation Research Board 80th annual meeting. Washington, DC).

    Google Scholar 

  • Gu, Y. (2004). Integrating a regional planning model (TRANSIMS) with an operational model (CORSIM). Master thesis, Virginia Polytechnic Institute and State University.

    Google Scholar 

  • Hamza-Lup, G. L., Hua, K. A., Peng, R. & Ho, A. H. (2005). A maximum flow approach to dynamic handling of multiple incidents in traffic evacuation management. (In Proceedings of the 2005 IEEE Intelligent Transportation Systems conference (pp. 1147–1152) ).

    Google Scholar 

  • Han, L. D. (2005). Evacuation modelling and operations using dynamic traffic assignment and most desirable destination approaches. (In Proceedings of the Transportation Research Board 84th annual meeting. Washington, DC).

    Google Scholar 

  • Han, L., Yuan, F., Chin, S. & Hwang, H. (2006). Global optimization of emergency evacuation assignments. Interfaces, 36, 502–513.

    Article  Google Scholar 

  • Jha, M., Moore, K., & Pashaie, B. (2004). Emergency evacuation planning with microscopic traffic simulation. Transportation Research Record, 1886, 40–48.

    Article  Google Scholar 

  • Kagaya, S., Uchida, K., Hagiwara, T. & Negishi, A. (2005). An application of multi-agent simulation to traffic behavior for evacuation in earthquake disaster. Journal of the Eastern Asia Society for Transportation Studies, 6, 4224–4236.

    Google Scholar 

  • Kang, J. E., Lindell, M. K. & Prater, C. S. (2007). Hurricane evacuation expectations and actual behavior in hurricane Lili. Journal of Applied Social Psychology, 37, 881–897.

    Article  Google Scholar 

  • Kwon E. & Pitt, S. (2005). Evaluation of emergency evacuation strategies for downtown event traffic using a dynamic network model. Transportation Research Record, 1922, 149–155.

    Article  Google Scholar 

  • Laska, S. & Morrow, B. H. (2006). Social vulnerabilities and hurricane Katrina: An unnatural disaster in New Orleans. Marine Technology Society Journal, 40, 16–26.

    Article  Google Scholar 

  • Lim, E. & Wolshon, B. (2005). Modeling and performance assessment of contraflow evacuation termination points. Transportation Research Record, 1922, 118–128.

    Article  Google Scholar 

  • Lindell, M.K. & Prater, C.S. (2007). Critical behavioral assumptions in evacuation analysis for private vehicles: Examples from hurricane research and planning. Journal of Urban Planning and Development, 133, 18–29.

    Article  Google Scholar 

  • Lindell, M. K., Prater, C. S., Sanderson, W. G., Lee, H. M., Yang, Z., Mohite, A. & Hwang, S. N. (2001). Texas gulf coast residents’ expectations and intentions regarding hurricane evacuation. Hazard Reduction & Recovery Center, Texas A&M University. College Station, TX.

    Google Scholar 

  • Liu, Y., Lai, X. R. & Chang, G. L. (2006). Two-level integrated optimization system for planning of emergency evacuation. Journal of Transportation Engineering, 132, 800–807.

    Article  Google Scholar 

  • Miller Consulting Inc. (2001). Florida Keys hurricane evacuation report. Contract No. C7391, Florida Department of Transportation. Miami, FL.

    Google Scholar 

  • Moeller, M., Urbanik, T. & Desrosiers, A. (1981). CLEAR (Calculated Logical Evacuation and Response): A generic transportation network evacuation model for the calculation of evacuation time estimates. Prepared for the Nuclear Regulatory Commission by Pacific Northwest Laboratory. Washington, DC.

    Google Scholar 

  • Murray-Tuite, P. M. & Mahmassani, H. S. (2004). Transportation network evacuation planning with household activity interactions. Transportation Research Record, 1894, 150–159.

    Article  Google Scholar 

  • Mysore, V., Narzisi, G. & Mishra, B. (2006). Emergency response planning for a potential sarin gas attack in Manhattan using agent-based models. Agent Technology for Disaster Management. Hakodate, Japan.

    Google Scholar 

  • Nelson, C. E., Coovert, M. D., Kurtz, A., Fritzche, B., Crumley, C. & Powell, A. (1989). Models of hurricane evacuation behavior. Department Of Psychology, University of South Florida, Tampa, FL.

    Google Scholar 

  • Peat, Marwick, Mitchell, & Company. (1973). Network flow simulation for urban traffic control system–Phase II. v. 1. Prepared for the Federal Highway Administration. Washington, DC.

    Google Scholar 

  • Planung Transport Verkehr AG (PTV) (2003). VISSIM3.7 user manual.

    Google Scholar 

  • Planung Transport Verkehr AG (PTV) (2004). VISSIM3.7 user manual.

    Google Scholar 

  • Post, Buckley, Schuh, & Jernigan Inc. (PBS&J) (2004). Galveston region hurricane transportation analysis 2004. Draft report. Prepared for US Army Corps of Engineers, Galveston District. Galveston, TX.

    Google Scholar 

  • Quadstone (2002). Quadstone Paramics V4.0 Modeller user guide.

    Google Scholar 

  • Radwan, E. & Ramasamy, S. (2003). I-4 corridor traffic simulation and visualization. Center for Advanced Transportation Systems Simulation, University of Central Florida, FL.

    Google Scholar 

  • Radwan, E., Mollaghasemi, M., Mitchell, S. & Yildirim, G. (2005). Framework for modelling emergency evacuation. Center for Advanced Transportation Systems Simulation, University of Central Florida, Orlando, FL.

    Google Scholar 

  • Rontiris, K. & Crous, W. (Undated). Emergency evacuation modelling for the Koeberg nuclear powers station [Electronic version]. Retrieved December 19, 2005, from http://www.inro.ca/en/pres_pap/asian/asi00/EMME2Asian.pdf.

  • Sinuany-stern, Z. & Stern, E. (1993). Simulating the evacuation of a small city–The effects of traffic factors. Socio-Economic Planning Sciences, 27, 97–108.

    Article  Google Scholar 

  • Sisiopiku, V. P., Jones, S., Sullivan, A. J., Patharkar, S. & Tang, X. (2004). Regional traffic simulation for emergency preparedness [Electronic version]. University Transportation Center for Alabama, The University of Alabama, The University of Alabama in Birmingham, and The University of Alabama in Huntsville. Retrieved December 20, 2005, from http://utca.eng.ua.edu/projects/final_reports/03226fnl.pdf.

  • Stern, E. & Sinuany-Stern, Z. (1989). A behavioral-based simulation model for urban evacuation. Papers in Regional Science, 66, 87–103.

    Article  Google Scholar 

  • Stern, E., Sinuany-Stern, Z. & Holm, Z. S. (1996). Congestion-related information and road network performance. Journal of Transport Geography, 4, 169–178.

    Article  Google Scholar 

  • Tagliaferri, A. P. (2005). Use and comparison of traffic simulation models in the analysis of emergency evacuation conditions. Master thesis, North Carolina State University.

    Google Scholar 

  • Texas Department of Public Safety (TXDPS) (2005). Evacuation maps for planning and public information: Houston-Galveston study area [Electronic version]. Retrieved December 19, 2005, from http://www.txdps.state.tx.us/dem/Hurricanemaps/GalvestonStudyAreaMap.pdf.

  • Theodoulou, G. & Wolshon, B. (2004). Alternative methods to increase the effectiveness of freeway contraflow evacuation. Transportation Research Record, 1865, 48–56.

    Article  Google Scholar 

  • Tweedie, S. W., Rowland, J. R., Walsh, S., Rhoten, R. R. & Hagle, P. L. (1986). A methodology for estimating emergency evacuation times. The Social Science Journal, 21, 189–204.

    Article  Google Scholar 

  • Williams, B. M., Tagliaferri, A. P., Meinhold, S. S., Hummer, J. E. & Rouphail, N. M. (2007) Simulation and annalysis of freeway lane reversal for coastal hurricane evacuation. Journal of Urban Planning and Development, 133, 61–72.

    Article  Google Scholar 

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Zhan, F.B., Chen, X. (2008). Agent-Based Modeling and Evacuation Planning. In: Sui, D.Z. (eds) Geospatial Technologies and Homeland Security. The GeoJournal Library, vol 94. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8507-9_9

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