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An aggregate approach to model evacuee behavior for no-notice evacuation operations

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Abstract

This study proposes an aggregate approach to model evacuee behavior in the context of no-notice evacuation operations. It develops aggregate behavior models for evacuation decision and evacuation route choice to support information-based control for the real-time stage-based routing of individuals in the affected areas. The models employ the mixed logit structure to account for the heterogeneity across the evacuees. In addition, due to the subjectivity involved in the perception and interpretation of the ambient situation and the information received, relevant fuzzy logic variables are incorporated within the mixed logit structure to capture these characteristics. Evacuation can entail emergent behavioral processes as the problem is characterized by a potential threat from the extreme event, time pressure, and herding mentality. Simulation experiments are conducted for a hypothetical terror attack to analyze the models’ ability to capture the evacuation-related behavior at an aggregate level. The results illustrate the value of using a mixed logit structure when heterogeneity is pronounced. They further highlight the benefits of incorporating fuzzy logic to enhance the prediction accuracy in the presence of subjective and linguistic elements in the problem.

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References

  • Abdelgawad, H., Abdulhai, B.: Managing large-scale multimodal emergency evacuations. J. Transp. Saf. Secur. 2(2), 122–151 (2010)

    Article  Google Scholar 

  • Alsnih, R., Stopher, P.R.: Review of procedures associated with devising emergency evacuation plans. Transp. Res. Rec. 1865, 79–89 (2004)

    Article  Google Scholar 

  • Baker, E.J.: Hurricane evacuation behavior. Int. J. Mass Emerg. Disasters 9(2), 287–310 (1991)

    Google Scholar 

  • Chiu, Y., Mirchandani, P.B.: Online behavior-robust feedback information routing strategy for mass evacuation. IEEE Trans. Intell. Transp. Syst. 9(2), 264–274 (2008)

    Article  Google Scholar 

  • Chiu, Y., Zheng, H., Villalobos, J., Gautam, B.: Modeling no-notice mass evacuation using a dynamic traffic flow optimization model. IIE Trans. 39, 83–94 (2007)

    Article  Google Scholar 

  • Eder, A.: After September 11, 2001: how transit agencies prepare for the threat of terrorism. Transp. Res. Rec. 1927, 92–100 (2005)

    Article  Google Scholar 

  • Fu, H., Wilmot, C.G.: Sequential logit dynamic travel demand model for hurricane evacuation. Transp. Res. Rec. 1882, 19–26 (2004)

    Article  Google Scholar 

  • Fu, H., Wilmot, C.G.: Survival analysis-based dynamic travel demand models for hurricane evacuation. Transp. Res. Rec. 1964, 211–218 (2006)

    Article  Google Scholar 

  • Hobeika, A.G., Kim, C.: Comparison of traffic assignments in evacuation modeling. IEEE Trans. Eng. Manag. 45(2), 192–198 (1998)

    Article  Google Scholar 

  • Kalafatas, G., Peeta, S.: Planning for evacuation: insights from an efficient network design model. J. Infrastruct. Syst. 15(1), 21–30 (2009)

    Article  Google Scholar 

  • Kang, J.E., Lindell, M.K., Prater, C.S.: Hurricane evacuation expectations and actual behavior in hurricane Lili. J. Appl. Psychol. 37(4), 887–903 (2007)

    Article  Google Scholar 

  • Lindell, M.K., Prater, C.S.: Critical behavioral assumptions in evacuation time estimate analysis for private vehicles: examples from hurricane research and planning. J. Urban Plan. Dev. 133(1), 18–29 (2007)

    Article  Google Scholar 

  • Lotan, T., Koutsopoulos, H.: Models for route choice behavior in the presence of information using concepts from fuzzy set theory and approximate reasoning. Transportation 20(2), 129–155 (1993)

    Article  Google Scholar 

  • Mawson, A.R.: Understanding mass panic and other collective responses to threat and disaster. Psychiatry 68(2), 95–113 (2005)

    Google Scholar 

  • Mileti, D.S., Peek, L.: The social psychology of public response to warnings of a nuclear power plant accident. J. Hazard. Mater. 75(2–3), 181–194 (2000)

    Article  Google Scholar 

  • Murray-Tuite, P.: Perspectives for network management in response to unplanned disruptions. J. Urban Plan. Dev. 133(1), 9–17 (2007)

    Article  Google Scholar 

  • Nozawa, M., Watanabe, T., Katada, N., Minami, H., Yamamoto, A.: Residents’ awareness and behaviour regarding typhoon evacuation advice in Hyogo Prefecture. Jpn. Int. Nurs. Rev. 55, 20–26 (2008)

    Article  Google Scholar 

  • Oak Ridge National Laboratory (ORNL): Oak Ridge Evacuation Modeling System (OREMS) 2.5 User Guide. Oak Ridge National Laboratory, Oak Ridge, TN (1998)

  • Pang, G.K.H., Takahashi, K., Yokota, T., Takenaga, H.: Adaptive route selection for dynamic route guidance system based on fuzzy-neural approaches. IEEE Trans. Veh. Technol. 48(6), 2028–2041 (1999)

    Article  Google Scholar 

  • Paz, A., Peeta, S.: Information-based network control strategies consistent with estimated driver behavior. Transp. Res. B 43(1), 73–96 (2009)

    Article  Google Scholar 

  • Peeta, S., Yu, J.W.: Data-consistent fuzzy approach for online driver behavior under information provision. Transp. Res. Rec. 1803, 76–86 (2002)

    Article  Google Scholar 

  • Peeta, S., Yu, J.W.: Adaptability of a hybrid route choice model to incorporating driver behavior dynamics under information provision. IEEE Trans. on Syst. 34(2), 243–256 (2004)

    Google Scholar 

  • Peeta, S., Ziliaskopoulos, A.K.: Foundations of dynamic traffic assignment: the past, the present and the future. Netw. Spat. Econ. 1(3–4), 233–256 (2001)

    Article  Google Scholar 

  • Prashker, J., Bekhor, S.: Route choice models used in the stochastic user equilibrium problem: a review. Transp. Rev. 24(4), 437–463 (2004)

    Article  Google Scholar 

  • Robinson, R.M., Khattak, A.: Route change decision making by hurricane evacuees facing congestion. Transp. Res. Rec. 2196, 168–175 (2010)

    Article  Google Scholar 

  • Sbayti, H., Mahmassani, H.S.: Optimal scheduling of evacuation operations. Transp. Res. Rec. 1964, 238–246 (2006)

    Article  Google Scholar 

  • Sheffi, Y., Mahmassani, H.S., Powell, W.B.: A transportation network evacuation model. Transp. Res. A 16(3), 209–218 (1982)

    Article  Google Scholar 

  • Sherali, H.D., Carter, T.B., Hobeika, A.G.: A location-allocation model and algorithm for evacuation planning under hurricane/flood conditions. Transp. Res. B 25(6), 439–452 (1991)

    Article  Google Scholar 

  • Song, W., Xu, X., Wang, B., Ni, S.: Simulation of evacuation processes using a multi-grid model for pedestrian dynamics. Phys. A 363, 492–500 (2006)

    Article  Google Scholar 

  • Stern, E.: Evacuation intentions of parents in an urban radiological emergency. Urban Stud 26(2), 191–198 (1989)

    Article  Google Scholar 

  • Teodorović, D., Vukanović, S., Obradović, K.: Modeling route choice with advanced traveler information by fuzzy logic. Transp. Plan. Technol. 22(1), 1–25 (1998)

    Article  Google Scholar 

  • Tierney, K.J., Lindell, M.K., Perry, R.W.: Facing the Unexpected: Disaster Preparedness and Response in the United States. Joseph Henry Press, Washington, DC (2001)

    Google Scholar 

  • Train, K.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge, MA (2003)

    Book  Google Scholar 

  • Turner, D.S., Evans, W.A., Kumlachew, M., Wolshon, B., Dixit, V., Sisiopiku, V.P., Islam, S., Anderson, M.D.: Issues, practices, and needs for communicating evacuation information to vulnerable populations. Transp. Res. Rec. 2196, 159–167 (2010)

    Article  Google Scholar 

  • Tuydes, H., Ziliaskopoulos, A.: Tabu-based heuristic approach for optimization of network evacuation contraflow. Transp. Res. Rec. 1964, 157–168 (2006)

    Article  Google Scholar 

  • Wilmot, C.G., Mei, B.: Comparison of alternative trip generation models for hurricane evacuation. Nat. Hazards Rev. 5(4), 170–178 (2004)

    Article  Google Scholar 

  • Wolshon, B.: Planning for the evacuation of New Orleans. ITE J. 72(2), 44–49 (2002)

    Google Scholar 

  • Xie, C., Lin, D., Waller, S.T.: A dynamic evacuation network optimization problem with lane reversal and crossing elimination strategies. Transp. Res. E 46(3), 295–316 (2010)

    Article  Google Scholar 

  • Ziliaskopoulos, A.K.: A linear programming model for the single destination system optimum dynamic traffic assignment problem. Transp. Sci. 34(1), 37–49 (2000)

    Article  Google Scholar 

Download references

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  1. School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN, 47907-2051, USA

    Yu-Ting Hsu & Srinivas Peeta

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  1. Yu-Ting Hsu
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  2. Srinivas Peeta
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Correspondence to Srinivas Peeta.

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Hsu, YT., Peeta, S. An aggregate approach to model evacuee behavior for no-notice evacuation operations. Transportation 40, 671–696 (2013). https://doi.org/10.1007/s11116-012-9440-7

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