Modeling emergency evacuation for major hazard industrial sites

Georgiadou, Paraskevi S.; Papazoglou, Ioannis A.; Kiranoudis, Chris T.; Markatos, N.C.

doi:10.1016/j.ress.2006.09.009

Cited by 78 publications

(33 citation statements)

References 30 publications

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“…Moreover, when multiple objectives are taken into consideration, it is difficult to determine the optimal evacuation routes straightforwardly in short time because of the unavoidable trade-offs between the multiple objectives. Georgiadou et al (2007) propose a Markovian stochastic model to study the dynamic movement of evacuees in order to provide optimal emergency response plans to minimize the health consequences of a toxic gas release accident. Georgiadou et al (2010) further take into account the socioeconomic costs of the emergency responses and develop a multi-objective optimization model for emergency response decision-making in hazardous chemical accidents (e.g., explosion accidents, toxic gas release accidents).…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated emergency response model for toxic gas release accidents based on cellular automata

Cao

Tian

et al. 2016

Ann Oper Res

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An integrated emergency response model based on cellular automata (CA) is proposed for the toxic gas release accidents that happen in the energy and chemical industry. This integrated emergency response model consists of three sub-models: a toxic gas dispersion model, a dynamic evaluation model for accident consequences, and an evacuation route selection model. When a toxic gas release accident happens, the dispersion model predicts the distribution of toxic gas concentration, the evaluation model estimates the consequences in terms of probability of death, expected fatalities and impact scope caused by the accident, and the route selection model provides the safest evacuation route for evacuees. The three sub-models run simultaneously and present real-time results. The proposed model is applied to an ammonia gas release accident in an energy and chemical enterprise, and the corresponding model results are discussed. The efficiency of emergency response for toxic gas release accidents can be further improved through the proposed integrated emergency response model based on CA.

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Section: Literature Reviewmentioning

confidence: 99%

An integrated emergency response model for toxic gas release accidents based on cellular automata

Cao

Tian

et al. 2016

Ann Oper Res

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“…In order to calculate the changes in population distribution in the evacuation space, a dynamic model based on the Markov random process is used to simulate the movement of the population. First a schematic diagram of the evacuation area is established, following the approach used in prior literature [17]. The key elements are as follows:…”

Section: The Basic Stochastic Markov Modelmentioning

confidence: 99%

“…To describe uncertainties, stochastic models based on the Markov approach have been widely applied in the field of traffic flow [16,17], but these models ignore subjective characteristics and only focus on objective information related to traffic network, such as distance and the congestion. In this work, the primary concern is large-scale crowd evacuation in which the physiological and psychological factors of the crowd itself are significant, affecting not only the objective parameters, e.g.…”

Section: Introductionmentioning

confidence: 99%

Randomness in the evacuation route selection of large-scale crowds under emergencies

Wang

Sun

2015

Applied Mathematical Modelling

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a b s t r a c tTo date, considerable research has been conducted into micro and macro evacuation models, fuelling the development of the field of emergency evacuation. However, the uncertainties of large-scale evacuation under emergencies have not yet been fully elucidated. In this paper, the impact of the crowd physiological and psychological factors on large-scale evacuation is presented and quantified, then incorporated into a random Markov route selection model of evacuees, to obtain a probabilistic description of crowd route selection. Against a background of instantaneous leakage and diffusion of CO poison gas, a detailed analysis of the uncertainties inherent in the evacuation process and results (specifically, the sensitivity of clearance time to a variety of factors) is conducted using the Markov process, integrating the initial distribution of crowds, route travelling capability, and so on. The variation rule of clearance time under the influence of relevant parameters is revealed and also the significance of considering the psychological and physiological risks in large-scale emergency evacuation is proved.

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“…Furthermore, evacuation dynamics for specific groups within a crowd, such as children, people with reduced mobility and individuals with disabilities, have been scrutinized in terms of movement patterns, including walking speed, flow through doors and stairs, as well as density around exits, and panic behavior [3][4][5][6][7]. Formal crowd models have previously been found useful for off-line escape planning in large and complex buildings as well as industrial sites frequented by a significant number of people [8][9][10][11][12][13]. In brief, the focus of previous work has typically been either on crowd behavior or on hazard dynamics, for fully known environments.…”

Section: Introductionmentioning

confidence: 99%

A spatio-temporal probabilistic model of hazard- and crowd dynamics for evacuation planning in disasters

et al. 2014

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Managing the uncertainties that arise in disasters-such as ship fire-can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this challenge, we propose a novel spatio-temporal probabilistic model that integrates crowd with hazard dynamics, using a ship fire as a proof-of-concept scenario. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior-both descriptive and normative (optimal). Descriptive modeling is based on studies of physical fire models, crowd psychology models, and corresponding flow models, while we identify optimal behavior using Ant-Based Colony Optimization (ACO). Simulation results demonstrate that the DNB model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Furthermore, the ACO provides safe paths, dynamically responding to current threats.

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Modeling emergency evacuation for major hazard industrial sites

Cited by 78 publications

References 30 publications

An integrated emergency response model for toxic gas release accidents based on cellular automata

An integrated emergency response model for toxic gas release accidents based on cellular automata

Randomness in the evacuation route selection of large-scale crowds under emergencies

A spatio-temporal probabilistic model of hazard- and crowd dynamics for evacuation planning in disasters

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