, Volume 45, Issue 1, pp 51–70 | Cite as

A statistical analysis of the dynamics of household hurricane-evacuation decisions

  • Md Tawfiq SarwarEmail author
  • Panagiotis Ch. Anastasopoulos
  • Satish V. Ukkusuri
  • Pamela Murray-Tuite
  • Fred L. Mannering


With the increasing number of hurricanes in the last decade, efficient and timely evacuation remains a significant concern. Households’ decisions to evacuate/stay and selection of departure time are complex phenomena. This study identifies the different factors that influence the decision making process, and if a household decides to evacuate, what affects the timing of the execution of that decision. While developing a random parameters binary logit model of the evacuate/stay decision, several factors, such as, socio-economic characteristics, actions by authority, and geographic location, have been considered along with the dynamic nature of the hurricane itself. In addition, taking the landfall as a base, how the evacuation timing varies, considering both the time-of-day and hours before landfall, has been analyzed rigorously. Influential factors in the joint model include the relative time until the hurricane’s landfall, height of the coastal flooding, and approaching speed of the hurricane; household’s geographic location (state); having more than one child in the household, vehicle ownership, and level of education; and type of evacuation notice received (voluntary or mandatory). Two time intervals from 30 to 42 h and 42 to 66 h before landfall resulted in random parameters, reflecting mixed effects on the likelihood to evacuate/stay. Possible sources of the unobserved heterogeneity captured by the random parameters include the respondents’ risk perception or other unobserved physiological and psychological factors associated with how respondents comprehend a hurricane threat. Thus, the model serves the purpose of estimating evacuation decision and timing simultaneously using the data of Hurricane Ivan.


Emergency management Dynamics of hurricane evacuation Joint modeling of evacuation decision and timing Unbalanced panel data Random parameters Binary logit model 



The research presented in this paper was supported by the National Science Foundation Awards SES 0826873 and CMMI 1520338; and CMMI 105544 for which the authors are grateful. However, the authors are solely responsible for the findings of the research work.


  1. Anastasopoulos, P.Ch., Mannering, F.L.: A note on modeling vehicle-accident frequencies with random parameter count models. Accid. Anal. Prev. 41(1), 153–159 (2009)CrossRefGoogle Scholar
  2. Anastasopoulos, P.Ch., Mannering, F.L.: An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data. Accid. Anal. Prev. 43(3), 1140–1147 (2011)CrossRefGoogle Scholar
  3. Anastasopoulos, P.Ch., Mannering, F.L.: Analysis of pavement overlay and replacement performance using random-parameters hazard-based duration models. J. Infrastruct. Syst. 21(1), 04014024 (2015)CrossRefGoogle Scholar
  4. Anastasopoulos, P.Ch., Labi, S., McCullouch, B.: Analyzing duration and prolongation of performance-based contracts using hazard-based duration and zero-inflated random parameters Poisson models. Transp. Res. Rec. 2136, 11–19 (2009)CrossRefGoogle Scholar
  5. Anastasopoulos, P.Ch., Labi, S., Karlaftis, M.G., Mannering, F.L.: Exploratory State-level empirical assessment of pavement performance. J. Infrastruct. Syst. 17(4), 200–215 (2011)CrossRefGoogle Scholar
  6. Anastasopoulos, P.Ch., Haddock, J.E., Karlaftis, M.G., Mannering, F.L.: Analysis of urban travel times: hazard-based approach to random parameters. Transp. Res. Rec. 2302, 121–129 (2012a)CrossRefGoogle Scholar
  7. Anastasopoulos, P.Ch., Karlaftis, M., Haddock, J., Mannering, F.L.: Household automobile and motorcycle ownership analyzed with random parameters bivariate ordered probit model. Transp. Res. Rec. 2279, 12–20 (2012b)CrossRefGoogle Scholar
  8. Anastasopoulos, P.Ch., Mannering, F.L., Haddock, J.: A random parameters seemingly unrelated equations approach to the post-rehabilitation performance of pavements. J. Infrastruct. Syst. 18(3), 176–182 (2012c)CrossRefGoogle Scholar
  9. Anastasopoulos, P.Ch., Mannering, F.L., Shankar, V.N., Haddock, J.E.: A study of factors affecting highway accident rates using the random-parameters tobit model. Accid. Anal. Prev. 45, 628–633 (2012d)CrossRefGoogle Scholar
  10. Baker, E.J.: Hurricane evacuation behavior. Int. J. Mass Emerg. Disasters 9(2), 287–310 (1991)Google Scholar
  11. Behnood, A., Roshandeh, A., Mannering, F.L.: Latent class analysis of the effects of age, gender and alcohol consumption on driver-injury severities. Anal. Methods Accid. Res. 3–4, 56–91 (2014)CrossRefGoogle Scholar
  12. Ben-Akiva, M., Walker, M., Bernardino, A., Gopinath, D., Morikawa, T., Polydoropoulos, A.: Integration of choice and latent variable models. In: Mahmassani, H. (ed.) In Perpetual Motion: Travel Behavior Research Opportunities and Application Challenges. Pergamon, New York (2002)Google Scholar
  13. Beven II, J.L., Avila, L.A., Blake, E.S., et al.: Atlantic hurricane season of 2005. Mon. Weather Rev. 136(3), 1109–1173 (2008). doi: 10.1175/2007MWR2074.1 CrossRefGoogle Scholar
  14. Bhat, C.: Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transp. Res. B 37(9), 837–855 (2003)CrossRefGoogle Scholar
  15. Dobler, C: Travel behaviour modelling for scenarios with exceptional events—methods and implementations, PhD Dissertation, IVT, ETH Zurich, Zurich (2013)Google Scholar
  16. Dobler, C., Kowald, M., Rieser-Schüssler, N., Axhausen, K.W.: Within-day replanning of exceptional events. Transp. Res. Rec. 2302, 138–147 (2012)CrossRefGoogle Scholar
  17. Franklin, J.L., Pasch, R.J., Avila, L.A., et al.: Atlantic hurricane season of 2004. Mon. Weather Rev. 134(3), 981–1025 (2006)CrossRefGoogle Scholar
  18. Fu, H., Wilmot, C.: Sequential logit dynamic travel demand model for hurricane evacuation. Transp. Res. Rec. 1882, 19–26 (2004)CrossRefGoogle Scholar
  19. Gladwin, H., Peacock, W.: Warning and evacuation: a night for hard houses. In: Peacock, W., Morrow, B., Gladwin, H. (eds.) Hurricane Andrew: Ethnicity, Gender and the Sociology of Disasters. Routledge, New York (1997)Google Scholar
  20. Gladwin, C., Gladwin, H., Peacock, W.: Modeling hurricane evacuation decisions with ethnographic methods. Int. J. Mass Emerg. Disasters 19(2), 117–143 (2001)Google Scholar
  21. Greene, W.: LIMDEP Version 9.0. Econometric Software Inc., Plainview (2007)Google Scholar
  22. Gudishala, R., Wilmot, C.: Comparison of time-dependent sequential logit and nested logit for modeling hurricane evacuation demand. Transp. Res. Rec. 2312, 134–140 (2012)CrossRefGoogle Scholar
  23. Hamed, M., Mannering, F.L.: Modeling travelers’ post-work activity involvement: toward a new methodology. Transp. Sci 27(4), 381–394 (1993)CrossRefGoogle Scholar
  24. Hasan, S., Ukkusuri, S.V., Gladwin, H., Murray-Tuite, P.: Behavioral model to understand household-level hurricane evacuation decision making. J Transp. Eng. ASCE 137(5), 341–348 (2011)CrossRefGoogle Scholar
  25. Hasan, S., Mesa-Arango, R., Ukkusuri, S.V.: A random-parameter hazard-based model to understand household evacuation timing behavior. Transp. Res. C 27, 108–116 (2013)CrossRefGoogle Scholar
  26. Koot, J.M., Kowald, M., Axhausen, K.W.: Modelling behaviour during a large-scale evacuation: a latent class model to predict evacuation behaviour, paper presented at the12th Swiss Transport Research Conference, Ascona, May 2012Google Scholar
  27. Lindell, M., Lu, J., Prater, C.: Household decision making and evacuation in response to Hurricane Lili. Nat. Hazards Rev. 6(4), 171–179 (2005)CrossRefGoogle Scholar
  28. Lindell, M., Kang, J., Prater, C.: The logistics of household hurricane evacuation. Nat. Hazards Rev. 58(3), 1093–1109 (2011)CrossRefGoogle Scholar
  29. McFadden, D.: Econometric models of probabilistic choice. In: Manski, C., McFadden, D. (eds.) Structural Analysis of Discrete Data with Econometric Applications. MIT Press, Cambridge (1981)Google Scholar
  30. Morrow, B., Gladwin, H.: Hurricane Ivan behavioral analysis, 2004 hurricane assessments. Federal Emergency Management Agency and US Army Corps of Engineers, Washington, DC (2005)Google Scholar
  31. Murray-Tuite, P., Wolshon, B.: Evacuation transportation modeling: an overview of research, development, and practice. Transp. Res C 27, 25–45 (2013)CrossRefGoogle Scholar
  32. Nelson, C., Crumley, C., Fritzsche, B., Adcock, B.: Lower Southeast Florida Hurricane Evacuation Study. US Army Corps of Engineers, Jacksonville, Florida (1989)Google Scholar
  33. Ozguven, E.E., Horner, M.W., Kocatepea, A., Marcelinb, J.M., Abdelraziga, Y., Sandoc, T., Mosesa, R.: Metadata-based needs assessment for emergency transportation operations with a focus on an aging population: a case study in Florida. Transp. Rev. (2015). doi: 10.1080/01441647.2015.1082516 Google Scholar
  34. Pel, A., Bliemer, M., Hoogendoorn, S.: A review on travel behaviour modelling in dynamic traffic simulation models for evacuations. Transportation 39(1), 97–123 (2012)CrossRefGoogle Scholar
  35. Petrolia, D., Bhattacharjee, S.: Why don’t coastal residents choose to evacuate for hurricanes? Coast. Manag. 38(2), 97–112 (2010)CrossRefGoogle Scholar
  36. Russo, B., Savolainen, P., Schneider, W., Anastasopoulos, P.Ch.: Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model. Anal. Methods Accid. Res. 2, 21–29 (2014)CrossRefGoogle Scholar
  37. Solis, D., Thomas, M., Letson, D.: An empirical evaluation of the determinants of household hurricane evacuation choice. J. Dev. Agric. Econ. 2(3), 188–196 (2010)Google Scholar
  38. Sorensen, J.: Hazard warning systems: review of 20 years of progress. Nat. Hazards Rev. 1(2), 119–125 (2000)CrossRefGoogle Scholar
  39. Stewart, S.R.: Tropical cyclone report-Hurricane Ivan 2-24 September 2004. National Hurricane Center, Miami (2005)Google Scholar
  40. Train, K.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge (2003)CrossRefGoogle Scholar
  41. Train, K: Halton sequences for mixed logit. Working paper. University of California, Department of Economics, Berkeley (1999)Google Scholar
  42. Washington, S.P., Karlaftis, M.G., Mannering, F.L.: Statistical and Econometric Methods for Transportation Data Analysis, 2nd edn. CRC Press, Boca Raton (2011)Google Scholar
  43. Whitehead, J.C., Edwards, B., Van Willigen, M., Maiolo, J.R., Wilson, K., Smith, K.: Heading for higher ground: factors affecting real and hypothetical hurricane evacuation behavior. Environ. Hazards 2(4), 133–142 (2000)CrossRefGoogle Scholar
  44. Widener, M.J., Horner, M.W., Metcalf, S.S.: Simulating the effects of social networks on a population’s hurricane evacuation participation. J. Geogr. Syst. 15, 193–209 (2013)CrossRefGoogle Scholar
  45. Wilmot, C.G., Mei, B.: Comparison of alternative trip generation models for Hurricane evacuation. Nat. Hazards Rev. 5(4), 170–178 (2004)CrossRefGoogle Scholar
  46. Wolshon, B.: Evacuation planning and engineering for Hurricane Katrina. Bridge 36(1), 27–34 (2006)Google Scholar
  47. Young, R.K., Liesman, J.: Estimating the relationship between measured wind speed and overturning truck crashes using a binary logit model. Accid. Anal. Prev. 39(3), 574–580 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Civil, Structural and Environmental Engineering, Engineering Statistics and Econometrics Application Research LaboratoryUniversity at Buffalo, The State University of New YorkBuffaloUSA
  2. 2.Department of Civil, Structural and Environmental Engineering, Institute for Sustainable Transportation and Logistics, Engineering Statistics and Econometrics Application Research LaboratoryUniversity at Buffalo, The State University of New YorkBuffaloUSA
  3. 3.Lyles School of Civil EngineeringPurdue UniversityWest LafayetteUSA
  4. 4.Department of Civil and Environmental EngineeringVirginia TechFalls ChurchUSA
  5. 5.Department of Civil and Environmental EngineeringUniversity of South FloridaTampaUSA

Personalised recommendations