Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from... more Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from such models are required to estimate the clearance times on which evacuation orders are based. The number of evacuees arriving at each destination also informs allocation of resources and shelter planning. Despite its importance, evacuee destination modeling has not received as much attention as identifying who evacuates and when. In this study, we present a new approach to identify evacuees and determine where they go and when using privacy-enhanced smartphone location data. We demonstrate the method using data from four recent U.S. hurricanes affecting multiple geographies (Florence 2018, Michael 2018, Dorian 2019, and Ida 2021). We then build on those results to develop a new machine learning model that predicts the number of evacuees that move between pairs of metropolitan statistical areas. The machine learning model incorporates hurricane characteristics, which have not been thoro...
Transportation Research Part B-methodological, Aug 1, 2021
Predicting evacuation related choices of households during a hurricane is of paramount importance... more Predicting evacuation related choices of households during a hurricane is of paramount importance to any emergency management system. Central to this problem is the identification of socio-demographic factors and hurricane characteristics that influence an individual’s decision to stay or evacuate. However, decision makers in such conditions do not make a single choice but constantly evaluate current and anticipated conditions before opting to stay or evacuate. We model this behavior using a finite-horizon dynamic discrete choice framework in which households may choose to evacuate or wait in time periods prior to a hurricane’s landfall. In each period, an individual’s utility depends not only on his/her current choices and the present values of the influential variables, but also involves discounted expected utilities from future choices should one decide to postpone their decision to evacuate. Assuming generalized extreme value (GEV) errors, a nested algorithm involving a dynamic program and a maximum likelihood method is used to estimate model parameters. Panel data on households affected by Hurricane Gustav was fused with the National Hurricane Center’s forecasts on the trajectory and intensity for the case study in the paper.
Today’s regional natural hazards loss models rarely incorporate changes in a region’s built envir... more Today’s regional natural hazards loss models rarely incorporate changes in a region’s built environment over time, and thus likely misestimate a region’s natural hazard risk. Of the existing natural hazard loss models that incorporate changes in the built environment, none are developed at an adequately granular spatiotemporal scale that is appropriate for regional (multi-county) natural hazards loss modeling. This work presents the new Housing Inventory Projection (HIP) method for estimating regional changes in a region’s housing inventory for natural hazards loss modeling purposes. The method is divided into two modules: (1) the Regional Annual County-Level Housing (REACH) module, which estimates the annual number of housing units per county over a multi-county region and multi-decadal projection period, and (2) the Single-family Location Estimation (SLE) module, which estimates the likely location of future single-family housing units across a subcounty grid space. While the HIP ...
The new Extended Optimization-Based Probabilistic Scenario method produces a small set of probabi... more The new Extended Optimization-Based Probabilistic Scenario method produces a small set of probabilistic ground motion maps to represent the seismic hazard for analysis of spatially distributed infrastructure. We applied the method to Christchurch, New Zealand, including a sensitivity analysis of key user-specified parameters. A set of just 124 ground motion maps were able to match the hazard curves based on a million-year Monte Carlo simulation with no error at the four selected return periods, mean spatial correlation errors of 0.03, and average error in the residential loss exceedance curves of 2.1%. This enormous computational savings in the hazard has substantial implications for regional-scale, policy decisions affecting lifelines or building inventories since it can allow many more downstream analyses and/or doing them using more sophisticated, computationally intensive methods. The method is robust, offering many equally good solutions and it can be solved using free open sou...
Critical infrastructure systems derive their importance from the societal needs they help meet. Y... more Critical infrastructure systems derive their importance from the societal needs they help meet. Yet the relationship between infrastructure system functioning and societal functioning is not well-understood, nor are the impacts of infrastructure system disruptions on consumers. We develop two empirical measures of societal impacts—willingness to pay (WTP) to avoid service interruptions and a constructed scale of unhappiness, compare them to each other and others from the literature, and use them to examine household impacts of service interruptions. Focusing on household-level societal impacts of electric power and water service interruptions, we use survey-based data from Los Angeles County, USA to fit a random effects within-between model of WTP and an ordinal logit with mixed effects to predict unhappiness, both as a function of infrastructure type, outage duration, and household attributes. Results suggest household impact increases nonlinearly with outage duration, and the impa...
Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from... more Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from such models are required to estimate the clearance times on which evacuation orders are based. The number of evacuees arriving at each destination also informs allocation of resources and shelter planning. Despite its importance, evacuee destination modeling has not received as much attention as identifying who evacuates and when. In this study, we present a new approach to identify evacuees and determine where they go and when using privacy-enhanced smartphone location data. We demonstrate the method using data from four recent U.S. hurricanes affecting multiple geographies (Florence 2018, Michael 2018, Dorian 2019, and Ida 2021). We then build on those results to develop a new machine learning model that predicts the number of evacuees that move between pairs of metropolitan statistical areas. The machine learning model incorporates hurricane characteristics, which have not been thoro...
Transportation Research Part B-methodological, Aug 1, 2021
Predicting evacuation related choices of households during a hurricane is of paramount importance... more Predicting evacuation related choices of households during a hurricane is of paramount importance to any emergency management system. Central to this problem is the identification of socio-demographic factors and hurricane characteristics that influence an individual’s decision to stay or evacuate. However, decision makers in such conditions do not make a single choice but constantly evaluate current and anticipated conditions before opting to stay or evacuate. We model this behavior using a finite-horizon dynamic discrete choice framework in which households may choose to evacuate or wait in time periods prior to a hurricane’s landfall. In each period, an individual’s utility depends not only on his/her current choices and the present values of the influential variables, but also involves discounted expected utilities from future choices should one decide to postpone their decision to evacuate. Assuming generalized extreme value (GEV) errors, a nested algorithm involving a dynamic program and a maximum likelihood method is used to estimate model parameters. Panel data on households affected by Hurricane Gustav was fused with the National Hurricane Center’s forecasts on the trajectory and intensity for the case study in the paper.
Today’s regional natural hazards loss models rarely incorporate changes in a region’s built envir... more Today’s regional natural hazards loss models rarely incorporate changes in a region’s built environment over time, and thus likely misestimate a region’s natural hazard risk. Of the existing natural hazard loss models that incorporate changes in the built environment, none are developed at an adequately granular spatiotemporal scale that is appropriate for regional (multi-county) natural hazards loss modeling. This work presents the new Housing Inventory Projection (HIP) method for estimating regional changes in a region’s housing inventory for natural hazards loss modeling purposes. The method is divided into two modules: (1) the Regional Annual County-Level Housing (REACH) module, which estimates the annual number of housing units per county over a multi-county region and multi-decadal projection period, and (2) the Single-family Location Estimation (SLE) module, which estimates the likely location of future single-family housing units across a subcounty grid space. While the HIP ...
The new Extended Optimization-Based Probabilistic Scenario method produces a small set of probabi... more The new Extended Optimization-Based Probabilistic Scenario method produces a small set of probabilistic ground motion maps to represent the seismic hazard for analysis of spatially distributed infrastructure. We applied the method to Christchurch, New Zealand, including a sensitivity analysis of key user-specified parameters. A set of just 124 ground motion maps were able to match the hazard curves based on a million-year Monte Carlo simulation with no error at the four selected return periods, mean spatial correlation errors of 0.03, and average error in the residential loss exceedance curves of 2.1%. This enormous computational savings in the hazard has substantial implications for regional-scale, policy decisions affecting lifelines or building inventories since it can allow many more downstream analyses and/or doing them using more sophisticated, computationally intensive methods. The method is robust, offering many equally good solutions and it can be solved using free open sou...
Critical infrastructure systems derive their importance from the societal needs they help meet. Y... more Critical infrastructure systems derive their importance from the societal needs they help meet. Yet the relationship between infrastructure system functioning and societal functioning is not well-understood, nor are the impacts of infrastructure system disruptions on consumers. We develop two empirical measures of societal impacts—willingness to pay (WTP) to avoid service interruptions and a constructed scale of unhappiness, compare them to each other and others from the literature, and use them to examine household impacts of service interruptions. Focusing on household-level societal impacts of electric power and water service interruptions, we use survey-based data from Los Angeles County, USA to fit a random effects within-between model of WTP and an ordinal logit with mixed effects to predict unhappiness, both as a function of infrastructure type, outage duration, and household attributes. Results suggest household impact increases nonlinearly with outage duration, and the impa...
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Papers by Rachel Davidson