A random-parameter hazard-based model to understand household evacuation timing behavior

Hasan, Samiul; Mesa-Arango, Rodrigo; Ukkusuri, Satish V.

doi:10.1016/j.trc.2011.06.005

Cited by 125 publications

(61 citation statements)

References 18 publications

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“…A simulation-based maximum likelihood method is usually used to estimate the parameters of the random parameter models. The simulation procedure involves drawing the values of the parameters (coefficients of the predictors) from a given predefined distribution (Hasan et al 2013). In this research, we assumed that the parameters follow a normal distribution, and these parameters of the statistically significant random variables are reported in terms of the mean and the standard deviation that describes the parameters' predefined distribution.…”

Section: Methodsmentioning

confidence: 99%

A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth

Mukherjee

Hastak

2017

Int J Disaster Risk Sci

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With the increased frequency of extreme weather events and large-scale disasters, extensive societal and economic losses incur every year due to damage of infrastructure and private properties, business disruptions, fatalities, homelessness, and severe health-related issues. In this article, we analyze the economic and disaster data from 1970 through 2010 to investigate the impact of disasters on country/region-level economic growth. We leveraged a random parameter modeling approach to develop the growth-econometrics model that identifies risk factors significantly influencing the country/region-level economic growth in the face of natural hazard-induced disasters, while controlling for country/region-and time-specific unobserved heterogeneities. We found that disaster intensity in terms of fatalities and homelessness, and economic characteristics such as openness to trade and a government's consumption share of purchasing power parity (PPP), are the significant risk factors that randomly vary for different countries/regions. Other significant factors found to be significant include population, real gross domestic product (GDP), and investment share of PPP converted GDP per capita. We also found that flood is the most devastating disaster to affect country/region-level economic growth. This growth-econometrics model will help in the policy and decision making of governments related to the investment needs for pre-and post-disaster risk mitigation and response planning strategies, to better protect nations and minimize disaster-induced economic impacts.

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Section: Methodsmentioning

confidence: 99%

A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth

Mukherjee

Hastak

2017

Int J Disaster Risk Sci

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“…This decision process is affected by multiple factors, such as the characteristics of a household [44][45][46][47][48][49][50][51][52], conditions in which they reside (e.g., income, type of house, vehicle ownership, time of day) [49,50,53,54], warning time [55,56], perceived information [57][58][59][60][61][62] and personal risk perception [63][64][65][66][67][68].…”

Section: Hurricane Evacuation Response Ratementioning

confidence: 99%

“…Among the factors, the characteristics of the household show a significant effect on the evacuation decisions [44][45][46][47][48][49][50], including its timing [51,68], destination [28,67] and mode [52]. One reason is that the characteristics of the household are consistently associated with risk perception.…”

Section: Hurricane Evacuation Response Ratementioning

confidence: 99%

“…A study compared the time-dependent nested logit model and the time-dependent sequential logit model, concluding that the time-dependent nested logit model is superior [49]. In order to understand the causal factors that influence the household-level evacuation timing decision, the random parameters are used in a hazard-based modeling approach [68]. The study concluded that, in addition to environmental, social and demographic factors, risk perception is another key element to understanding the influence of warning response in an evacuation setting.…”

Section: Hurricane Evacuation Response Ratementioning

confidence: 99%

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Optimization of Evacuation Warnings Prior to a Hurricane Disaster

et al. 2017

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Abstract:The key purpose of this paper is to demonstrate that optimization of evacuation warnings by time period and impacted zone is crucial for efficient evacuation of an area impacted by a hurricane. We assume that people behave in a manner consistent with the warnings they receive. By optimizing the issuance of hurricane evacuation warnings, one can control the number of evacuees at different time intervals to avoid congestion in the process of evacuation. The warning optimization model is applied to a case study of Hurricane Sandy using the study region of Brooklyn. We first develop a model for shelter assignment and then use this outcome to model hurricane evacuation warning optimization, which prescribes an evacuation plan that maximizes the number of evacuees. A significant technical contribution is the development of an iterative greedy heuristic procedure for the nonlinear formulation, which is shown to be optimal for the case of a single evacuation zone with a single evacuee type case, while it does not guarantee optimality for multiple zones under unusual circumstances. A significant applied contribution is the demonstration of an interface of the evacuation warning method with a public transportation scheme to facilitate evacuation of a car-less population. This heuristic we employ can be readily adapted to the case where response rate is a function of evacuation number in prior periods and other variable factors. This element is also explored in the context of our experiment.

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“…The loss of lives from these devastating hurricanes highlights the importance of effective evacuation strategy. Hurricane evacuation is a complex dynamic process governed by many factors such as hurricane trajectory, household locations, warning system, and characteristics of the evacuees and their households [18]. Although mandatory or voluntary evacuation orders are issued by the authorities in the wake of a hurricane due to the anticipated storm surge, many people defy these orders risking their lives [2,9].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Geo-tagged Tweets to Understand Evacuation Dynamics during Emergencies

Kumar

Ukkusuri

2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Hurricane evacuation is a complex process and a better understanding of the evacuation behavior of the coastal residents could be helpful in planning better evacuation policy. Traditionally, various aspects of the household evacuation decisions have been determined by post-evacuation questionnaire surveys, which are usually time-consuming and expensive. Increased activity of users on social media, especially during emergencies, along with the geo-tagging of the posts, provides an opportunity to gain insights into user's decision-making process, as well as to gauge public opinion and activities using the social media data as a supplement to the traditional survey data. This paper leverages the geo-tagged Tweets posted in the New York City (NYC) in wake of Hurricane Sandy to understand the evacuation behavior of the residents. Based on the geo-tagged Tweet locations, we classify the NYC Twitter users into one of the three categories: outside evacuation zone, evacuees, and non-evacuees and examine the types of Tweets posted by each group during different phases of the hurricane. We establish a strong link between the social connectivity with the decision of the users to evacuate or stay. We analyze the geo-tagged Tweets to understand evacuation and return time and evacuation location patterns of evacuees. The analysis presented in this paper could be useful for authorities to plan a better evacuation campaign to minimize the risk to the life of the residents of the emergency hit areas.

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A random-parameter hazard-based model to understand household evacuation timing behavior

Cited by 125 publications

References 18 publications

A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth

A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth

Optimization of Evacuation Warnings Prior to a Hurricane Disaster

Utilizing Geo-tagged Tweets to Understand Evacuation Dynamics during Emergencies

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