A random parameter ordered probit model to understand the mobilization time during hurricane evacuation

This paper presents a random parameters ordered probit model to capture underlying unobserved characteristics in the timing behavior of the evacuees that elapses in between their evacuation decision and actual evacuation i.e. the mobilization time for an evacuee. The ordered probit model has been developed by using Hurricane Ivan data and the estimation findings suggest that the mobilization time involves a complex interaction of variables related to household location, evacuation characteristics, and socio-economic characteristics among others. In the model, six variables- source and time of evacuation notice received, work constraint, previous hurricane experience, race and income- were found to be random and the random parameters (all normally distributed) suggest that their effect varies across the observations. In addition, the model introduces some new factors that impact the mobilization time (for example, the mobilization time for evacuees evacuating to public shelters is significantly lower) which have not been found in the earlier literature to the best of our knowledge. The findings of this study are useful to determine different fractions of people evacuating early or delaying for some time once they actually decide to evacuate, for a given socio-demographic profile. These fractions can be used in the future to develop more accurate dynamic travel demands for use in traffic simulation models.

[1]  Brian Wolshon,et al.  PLANNING FOR THE EVACUATION OF NEW ORLEANS , 2002 .

[2]  Asad J. Khattak,et al.  Applying the Ordered Probit Model to Injury Severity in Truck-Passenger Car Rear-End Collisions , 1998 .

[3]  Chester G. Wilmot,et al.  Sequential Logit Dynamic Travel Demand Model for Hurricane Evacuation , 2004 .

[4]  Vinayak Dixit,et al.  Understanding the Impact of a Recent Hurricane on Mobilization Time during a Subsequent Hurricane , 2008 .

[5]  Michael K. Lindell,et al.  Critical Behavioral Assumptions in Evacuation Time Estimate Analysis for Private Vehicles: Examples from Hurricane Research and Planning , 2007 .

[6]  A. Beck,et al.  Human and pet-related risk factors for household evacuation failure during a natural disaster. , 2001, American journal of epidemiology.

[7]  Chester G. Wilmot,et al.  Survival analysis-based dynamic travel demand models for hurricane evacuation , 2006 .

[8]  Pamela Murray-Tuite,et al.  Household-level model for hurricane evacuation destination type choice using hurricane Ivan data , 2013 .

[9]  Arif Mohaimin Sadri.  Behavioral models to understand routing considerations and evacuation preparation time in hurricanes , 2012 .

[10]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[11]  Pamela Murray-Tuite,et al.  Behavioral Model to Understand Household-Level Hurricane Evacuation Decision Making , 2011 .

[12]  Pamela Murray-Tuite,et al.  Evacuation transportation modeling: An overview of research, development, and practice , 2013 .

[13]  Pamela Murray-Tuite,et al.  How to evacuate: model for understanding the routing strategies during hurricane evacuation , 2014 .

[14]  Earl J. Baker,et al.  Predicting Response to Hurricane Warnings - Reanalysis of Data from 4 Studies , 1979 .

[15]  Brian Wolshon,et al.  Modeling Risk Attitudes in Evacuation Departure Choices , 2012 .

[16]  Nicole Dash,et al.  Evacuation Decision Making and Behavioral Responses: Individual and Household , 2007 .

[17]  Asad J. Khattak,et al.  Route Change Decision Making by Hurricane Evacuees Facing Congestion , 2010 .

[18]  C. Bhat.  Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences , 2003 .

[19]  John E. Haddock,et al.  Household Automobile and Motorcycle Ownership Analyzed with Random Parameters Bivariate Ordered Probit Model , 2012 .

[20]  J. H. Sorensen,et al.  When Shall We Leave?: Factors Affecting the Timing of Evacuation Departures , 1991 .

[21]  S. Cutter,et al.  Crying wolf: Repeat responses to hurricane evacuation orders , 1998 .

[22]  E. Baker,et al.  Destination Choice Model for Hurricane Evacuation , 2008 .

[23]  D. McFadden,et al.  ESTIMATION BY SIMULATION , 1994 .

[24]  Bm Voght.  Issues in Nursing Home Evacuations , 1991 .

[25]  Fred L. Mannering,et al.  HAZARD-BASED DURATION MODELS AND THEIR APPLICATION TO TRANSPORT ANALYSIS. , 1994 .

[26]  Earl J. Baker,et al.  Hurricane Evacuation Behavior , 1991, International Journal of Mass Emergencies & Disasters.

[27]  Satish V. Ukkusuri,et al.  A random-parameter hazard-based model to understand household evacuation timing behavior , 2013 .

[28]  Hannah Smitherman,et al.  Special needs of children following a disaster , 2002 .

[29]  Steven Stern,et al.  Simulation-based estimation , 1997 .

[30]  Walter Gillis Peacock,et al.  Social Science Research Needs for the Hurricane Forecast and Warning System , 2007 .

[31]  Fred L Mannering,et al.  A note on modeling vehicle accident frequencies with random-parameters count models. , 2009, Accident; analysis and prevention.

[32]  Pamela Murray-Tuite,et al.  Changes in Evacuation Decisions between Hurricanes Ivan and Katrina , 2012 .

[33]  C. K. Mertz,et al.  Gender, race, and perception of environmental health risks. , 1994, Risk analysis : an official publication of the Society for Risk Analysis.

[34]  Michael K. Lindell,et al.  Household Decision Making and Evacuation in Response to Hurricane Lili , 2005 .

[35]  R. McKelvey,et al.  A statistical model for the analysis of ordinal level dependent variables , 1975 .

[36]  M. Ben-Akiva,et al.  Discrete choice analysis , 1989 .