PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Mohammad Pishahang Co-author(s): Andres Ruiz-Tagle, aruiztag@umd.edu Enrique Lopez Droguett, eald@g.ucla.edu Marilia Ramos, mariliar@g.ucla.edu Ali Mosleh, mosleh@g.ucla.edu

Presenter of this paper: Marilia Ramos (marilia.ramos@ucla.edu)

WISE: a probabilistic wildfire egress planning framework
Wildfire is a significant threat to many communities in Wildland Urban Interface (WUI) areas, and ensuring an efficient evacuation of these communities in case of wildfire is a pressing challenge. Wildfire evacuation modeling consists of three main layers: fire model, human decision-making, and traffic models. An efficient evacuation planning needs thus a comprehensive understanding of each of these layers and their mutual interactions. Numerous methods have been proposed for wildfire risk assessment, focusing on each of these components, but few address the issue considering all these layers. This paper presents a framework for probabilistic evacuation planning in the case of wildfires. The Wildfire Safe Egress (WISE) framework integrates a human decision model, a traffic model, and wildfire dynamics modeling for estimating the probability that a community safely evacuates when in danger by a wildfire. The evacuation success is calculated through a comparison between two competing variables. The Available Safe Egress Time (ASET) determines the total amount of time before the fire reaches a community's borders. This variable depends on the wildfire dynamics. The Required Safe Egress Time (RSET) determines the amount of time a community needs to evacuate safely. The RSET considers population distribution, demographic characteristics, warning system timing and its reliability, available roads network, and the traffic travel times. These variables are modeled in a Bayesian Belief Network (BBN). Next, a Monte Carlo simulation of a Poisson process defined by the community's socio-demographic profile generates the evacuation demand curve. Finally, the traffic model is developed through agent-based modeling of evacuees mobilization on the roads network. The final node of the BBN estimates the probability of a successful evacuation. Having a realistic estimation of this probability helps decision-makers and stakeholders to plan evacuation time, routes, and strategies to mitigate the consequences of a wildfire on a community considering different scenarios. This framework is implemented as a web platform, allowing users to have a practical egress assessment in a visual GIS-based environment.

WISE: a probabilistic wildfire egress planning framework

Wildfire is a significant threat to many communities in Wildland Urban Interface (WUI) areas, and ensuring an efficient evacuation of these communities in case of wildfire is a pressing challenge. Wildfire evacuation modeling consists of three main layers: fire model, human decision-making, and traffic models. An efficient evacuation planning needs thus a comprehensive understanding of each of these layers and their mutual interactions. Numerous methods have been proposed for wildfire risk assessment, focusing on each of these components, but few address the issue considering all these layers. This paper presents a framework for probabilistic evacuation planning in the case of wildfires. The Wildfire Safe Egress (WISE) framework integrates a human decision model, a traffic model, and wildfire dynamics modeling for estimating the probability that a community safely evacuates when in danger by a wildfire. The evacuation success is calculated through a comparison between two competing variables. The Available Safe Egress Time (ASET) determines the total amount of time before the fire reaches a community's borders. This variable depends on the wildfire dynamics. The Required Safe Egress Time (RSET) determines the amount of time a community needs to evacuate safely. The RSET considers population distribution, demographic characteristics, warning system timing and its reliability, available roads network, and the traffic travel times. These variables are modeled in a Bayesian Belief Network (BBN). Next, a Monte Carlo simulation of a Poisson process defined by the community's socio-demographic profile generates the evacuation demand curve. Finally, the traffic model is developed through agent-based modeling of evacuees mobilization on the roads network. The final node of the BBN estimates the probability of a successful evacuation. Having a realistic estimation of this probability helps decision-makers and stakeholders to plan evacuation time, routes, and strategies to mitigate the consequences of a wildfire on a community considering different scenarios. This framework is implemented as a web platform, allowing users to have a practical egress assessment in a visual GIS-based environment.

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Author and Presentation Info

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Presenter Name: Marilia Ramos (marilia.ramos@ucla.edu)

Bio:

Country: United States of America
Company: University of California Los Angeles
Job Title: Research Scientist

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Paper MH163 Preview

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