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1. Data-Driven Modeling of Evacuation Decision-Making in Extreme Weather Events

   Hancock, Matthew ; Halim, Nafisa ; Kuhlman, Chris J. ; Marathe, Achla ; Mozumder, Pallab ; Ravi, S.S. ; Vullikanti, Anil ( January 2021 , Complex Networks and their Applications)

   null (Ed.)

   Data from surveys administered after Hurricane Sandy provide a wealth of information that can be used to develop models of evacuation decision-making. We use a model based on survey data for predicting whether or not a family will evacuate. The model uses 26 features for each household including its neighborhood characteristics. We augment a 1.7 million node household-level synthetic social network of Miami, Florida with public data for the requisite model features so that our population is consistent with the survey-based model. Results show that household features that drive hurricane evacuations dominate the eects of specifying large numbers of families as "early evacuators" in a contagion process, and also dominate effects of peer influence to evacuate. There is a strong network-based evacuation suppression effect from the fear of looting. We also study spatial factors aecting evacuation rates as well as policy interventions to encourage evacuation. 

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2. Increasing Evacuation During Disaster Events

   Kuhlman, Chris J ; Marathe, Achla ; Vullikanti, Anil ; Halim, Nafisa ; Mozumder, Pallab ( May 2020 , Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems)

   null (Ed.)

   Timely evacuation is a standard recommendation by local agencies before disaster events such as hurricanes, which have enough advance notice. However, it has been observed in many recent disasters (e.g., Sandy), that only a small fraction of the population evacuates in time. Recent work by social scientists has examined the factors that influence household evacuation decisions; in addition to individual factors it has been found that peer effect plays a role in this decision but in two opposing ways. Specifically, households are motivated to evacuate if their neighbors evacuate. However, if too many neighbors leave then some households have concerns of looting and crime, and they choose not to evacuate. This makes the dynamics of evacuation very complex. In this paper, we use a detailed agent based model to study the dynamics of evacuation in Virginia’s coastal region. We use data from a large survey and social contagion and collective action theories to develop the model. We evaluate different strategies to increase evacuation. 

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3. A Predictive Analytics Approach for Nursing Home Hurricane Evacuation

   Sakib, Nazmus ; Hyer, Kathryn ; Dobbs, Debra ; Peterson, Lindsay ; Jester, Dylan J. ; Kong, N ( July 2019 , Proceedings of the 2019 IISE Annual Conference)

   Whether to evacuate a nursing home (NH) or shelter in place in response to the approaching hurricane is one of the most complex and difficult decisions encountered by nursing home administrators. A variety of factors may affect the evacuation decision, including storm and environmental conditions, nursing home characteristics, and the dwelling residents’ health conditions. Successful prediction of evacuation decision is essential to proactively prepare and manage resources to meet the surge in nursing home evacuation demands. In current nursing home emergency preparedness literature, there is a lack of analytical models and studies for nursing home evacuation demand prediction. In this paper, we propose a predictive analytics framework by applying machine learning techniques, integrated with domain knowledge in NH evacuation research, to extract, identify and quantify the effects of relevant factors on NH evacuation from heterogeneous data sources. In particular, storm features are extracted from Geographic Information System (GIS) data to strengthen the prediction accuracy. To further illustrate the proposed work and demonstrate its practical validity, a real-world case study is given to investigate nursing home evacuation in response to recent Hurricane Irma in Florida. The prediction performance among different predictive models are also compared comprehensively. 

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4. Household hurricane evacuation during a dual-threat event: Hurricane Laura and COVID-19

   https://doi.org/10.1016/j.trd.2023.103820  

   Greer, Alex ; Huntsman, David ; Wu, Hao-Che ; Murphy, Haley ; Clay, Lauren ( August 2023 , Transportation Research Part D: Transport and Environment)

   Cao, Jason Xinyu ; Ge, Ying-En (Ed.)

   This study explores household-level evacuation decision-making in response to Hurricane Laura, in a context where hurricane risk reduction measures contradicted COVID-19 risk reduction measures. Data were collected using a mail-based survey approach from households along the coast of Texas and Louisiana to explore drivers of and barriers to evacuation, including COVID-19 measures such as negative affect, risk perceptions, protective actions, and exposure. Testing for direct and indirect effects among the drivers of and barriers to evacuation, we find that many of our COVID-19 measures did not have a direct effect on evacuation but did have indirect effects through other factors. We also found evidence of both direct and indirect relationships with regards to more conventional drivers of evacuation found in the literature. We close with a discussion of the limitations and implications of this study. 

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5. A Computational Model of Hurricane Evacuation Decision.

   Yongsatianchot, N. ; Marsella, S. ( January 2020 , 19th International Conference on Autonomous Agents and MultiAgent Systems.)

   Hurricanes are devastating natural disasters. In deciding how to respond to a hurricane, in particular whether and when to evacuate, a decision-maker must weigh often highly uncertain and contradic- tory information about the future path and intensity of the storm. To effectively plan to help people during a hurricane, it is crucial to be able to predict and understand this evacuation decision. To this end, we propose a computational model of human sequential decision-making in response to a hurricane based on a Partial Ob- servable Markov Decision Process (POMDP) that models concerns, uncertain beliefs about the hurricane, and future information. We evaluate the model in two ways. First, hurricane data from 2018 was used to evaluate the model’s predictive ability on real data. Second, a simulation study was conducted to qualitatively evaluate the sequential aspect of the model to illustrate the role that the acquisition of future, more accurate information can play on cur- rent decision-making. The evaluation with 2018 hurricane season data shows that our proposed features are significant predictors and the model can predict the data well, within and across distinct hurricane datasets. The simulation results show that, across dif- ferent setups, our model generates predictions on the sequential decisions making aspect that align with expectations qualitatively and suggests the importance of modeling information. 

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