Evacuation model

Last updated

Evacuation models are simulation tools designed to predict the movement and behaviour of individuals during an emergency evacuation. [1] [2] These models are today used to simulate evacuations for several disasters, such as building fires, wildfires, hurricanes, and tsunamis. Thes models have been under development since the late 1970s and they are now widely to assess the time required to evacuate buildings, cities or wider regions. [3] [4] [5] [6]

Contents

A small-scale simulation run on FDS+Evac. The simulation of a classroom Evacuation Model Classroom.gif
A small-scale simulation run on FDS+Evac. The simulation of a classroom
Evacuation simulation of a bottleneck using FES+Evac Evacuation bottleneck.gif
Evacuation simulation of a bottleneck using FES+Evac

History

The earliest computer-based evacuation models, such as EVACNET (developed in the late 1970s), FPETool (introduced in 1990), and EXIT89 (from the 1980s), were developed in response to the growing need for accurate assessment of evacuation times. [1] These models emerged to address limitations in manual evacuation calculations, particularly as building designs and fire safety regulations became more complex. EVACNET focused on using network optimisation to reduce congestion during evacuations. [9] FPETool, developed by NIST, provided detailed predictions of fire behaviour, smoke spread, and egress times to aid fire safety engineers. [10] EXIT89, developed by Dr Rita Fahy, added a behavioural dimension, simulating how individuals might respond to evacuation orders. [11] These models paved the way for more advanced simulations by automating critical safety assessments and optimising building evacuation strategies

Simulation scale

Small-scale models, typically used for building evacuations, focus on individual or group dynamics within confined environments, such as offices, residential buildings, or public spaces, taking into account factors like building layout, fire spread, and occupant behaviour. These models often incorporate agent-based or microscopic approaches to simulate detailed interactions and decision-making processes. [12] [1] [13] One of the last surveys shows that there are 72 small-scale evacuation models currently in use for fire evacuation. [3]

In contrast, large-scale evacuation models deal with mass evacuations from broader areas, such as urban environments or regions affected by natural disasters like wildfires or earthquakes. These models emphasise traffic flow, route optimisation and infrastructure capacity – addressing the logistical challenges of moving large populations over significant distances. [2] [14] [15]

Simulation resolution

The simulation resolution in evacuation models refers to the level of detail and granularity used to represent evacuees and their environment during a simulation. [16] [17]

At the microscopic scale, each individual is modelled as an independent agent with unique characteristics such as speed, decision-making abilities, and interactions with others, making this approach ideal for detailed simulations of small spaces like buildings. [18]

Macroscopic models, on the other hand, treat people as a collective flow, using principles similar to fluid dynamics to represent large crowds or populations in more general terms, often applied to large-scale evacuations such as citywide scenarios. [19]

Mesoscopic models bridge the gap between these two. This approach represents groups of individuals as a collective unit while maintaining some individual behaviours, making them useful for medium-sized environments or scenarios where detailed interaction is less important than overall flow. [20]

The choice of simulation scale is crucial in balancing model complexity, computational cost and the specific goals of the evacuation study. [21]

Movement representation

Movement representation refers to how the physical movement of evacuees is simulated within a space, influencing the accuracy and realism of the model. [22] [23] [24]

Grid-based models divide the environment into discrete cells, with individuals moving from one cell to another based on simple rules, often used in cellular automata approaches. These models are effective for simulating movement in structured environments like corridors but can be limited in capturing fluid, natural movement. [25] [26]

Continuous models provide a more flexible representation, allowing evacuees to move freely in any direction within a continuous space. These models are often used with agent-based or force-based simulations, where individuals adjust their speed and direction based on personal preferences, obstacles, and interactions with others. [23] [27]

Network-based models abstract the environment into nodes and links, where movement is simplified to navigating from one point to another along predefined paths, commonly used in large-scale scenarios like transportation networks. [28] [29]

Each method of movement representation has strengths and is chosen based on the environment's complexity, required accuracy and computational efficiency. [21]

Use

Small-scale evacuation models are used to simulate and analyze how people evacuate buildings or outdoor environments in emergency situations. These models are essential tools for fire safety engineering, urban planning, and emergency preparedness. [4] [5] They help in assessing the effectiveness of building designs, evacuation routes, and safety procedures by representing how individuals or crowds move and behave during evacuations. They are commonly used in complex, high-occupancy environments like train or metro stations, shopping malls, arenas or stadiums, high-rise buildings, and residential or office buildings. These models play a crucial role in ensuring the safety and efficiency of evacuation procedures. [3] Their primary application is to ensure compliance with building codes and safety standards, particularly in structures where prescriptive fire regulations may not be easily met. [3]

Large-scale evacuation models are primarily used by emergency management agencies, urban planners, transportation authorities, and event security teams to plan, simulate, and optimize evacuation strategies during disasters, industrial accidents, or large public events. [6] These models are use to predict traffic flow, identify bottlenecks, and evaluate different evacuation routes or traffic management strategies. [15] Event planners and security personnel also rely on evacuation models to ensure the safety of large crowds during mass gatherings, enabling them to plan efficient exits in case of emergencies. These models are also utilized by governments and civil protection agencies to develop evacuation plans for cities, ensuring that evacuation routes are effective under various emergency scenarios.

Related Research Articles

<span class="mw-page-title-main">Simulation</span> Imitation of the operation of a real-world process or system over time

A simulation is an imitative representation of a process or system that could exist in the real world. In this broad sense, simulation can often be used interchangeably with model. Sometimes a clear distinction between the two terms is made, in which simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Another way to distinguish between the terms is to define simulation as experimentation with the help of a model. This definition includes time-independent simulations. Often, computers are used to execute the simulation.

A discrete element method (DEM), also called a distinct element method, is any of a family of numerical methods for computing the motion and effect of a large number of small particles. Though DEM is very closely related to molecular dynamics, the method is generally distinguished by its inclusion of rotational degrees-of-freedom as well as stateful contact, particle deformation and often complicated geometries. With advances in computing power and numerical algorithms for nearest neighbor sorting, it has become possible to numerically simulate millions of particles on a single processor. Today DEM is becoming widely accepted as an effective method of addressing engineering problems in granular and discontinuous materials, especially in granular flows, powder mechanics, ice and rock mechanics. DEM has been extended into the Extended Discrete Element Method taking heat transfer, chemical reaction and coupling to CFD and FEM into account.

<span class="mw-page-title-main">Molecular dynamics</span> Computer simulations to discover and understand chemical properties

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanical force fields. The method is applied mostly in chemical physics, materials science, and biophysics.

<span class="mw-page-title-main">Computer simulation</span> Process of mathematical modelling, performed on a computer

Computer simulation is the running of a mathematical model on a computer, the model being designed to represent the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics, astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.

<span class="mw-page-title-main">Crowd simulation</span> Model of movement

Crowd simulation is the process of simulating the movement of a large number of entities or characters. It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training, architecture and urban planning, and evacuation simulation.

<span class="mw-page-title-main">Crowd</span> Group who have gathered for a common purpose or intent

A crowd is as a group of people that have gathered for a common purpose or intent. Examples are a demonstration, a sports event, or a looting. A crowd may also simply be made up of many people going about their business in a busy area.

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

<span class="mw-page-title-main">Emergency evacuation</span> Urgent removal of people from an area of imminent or ongoing threat

Emergency evacuation is an immediate egress or escape of people away from an area that contains an imminent threat, an ongoing threat or a hazard to lives or property.

<span class="mw-page-title-main">Evacuation simulation</span>

Evacuation simulation is a method to determine evacuation times for areas, buildings, or vessels. It is based on the simulation of crowd dynamics and pedestrian motion. The number of evacuation software have been increased dramatically in the last 25 years. A similar trend has been observed in term of the number of scientific papers published on this subject. One of the latest survey indicate the existence of over 70 pedestrian evacuation models. Today there are two conferences dedicated to this subject: "Pedestrian Evacuation Dynamics" and "Human Behavior in Fire".

<span class="mw-page-title-main">PTV Vissim</span> Traffic flow simulation software

PTV Vissim is a microscopic multi-modal traffic flow simulation software package developed by PTV Planung Transport Verkehr AG in Karlsruhe, Germany. It was first developed in 1992. The name is derived from "Verkehr In Städten - SIMulationsmodell".

<span class="mw-page-title-main">Wildfire modeling</span>

Wildfire modeling is concerned with numerical simulation of wildfires to comprehend and predict fire behavior. Wildfire modeling aims to aid wildfire suppression, increase the safety of firefighters and the public, and minimize damage. Wildfire modeling can also aid in protecting ecosystems, watersheds, and air quality.

Agent-based social simulation consists of social simulations that are based on agent-based modeling, and implemented using artificial agent technologies. Agent-based social simulation is a scientific discipline concerned with simulation of social phenomena, using computer-based multiagent models. In these simulations, persons or group of persons are represented by agents. MABSS is a combination of social science, multiagent simulation and computer simulation.

Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences and affiliate of the Computer Science Department at ETH Zurich.

<span class="mw-page-title-main">UC Irvine Institute of Transportation Studies</span> Research unit of UC Irvine

The UC Irvine Institute of Transportation Studies (ITS), is a University of California organized research unit with sister branches at UC Berkeley, UC Davis, and UCLA. ITS was established to foster research, education, and training in the field of transportation. UC Irvine ITS is located on the fourth floor of the Anteater Instruction and Research Building at University of California, Irvine's main Campus, and also houses the UC Irvine Transportation Science graduate studies program.

OpenWorm is an international open science project for the purpose of simulating the roundworm Caenorhabditis elegans at the cellular level. Although the long-term goal is to model all 959 cells of the C. elegans, the first stage is to model the worm's locomotion by simulating the 302 neurons and 95 muscle cells. This bottom up simulation is being pursued by the OpenWorm community.

<span class="mw-page-title-main">Microscale and macroscale models</span> Classes of computational models

Microscale models form a broad class of computational models that simulate fine-scale details, in contrast with macroscale models, which amalgamate details into select categories. Microscale and macroscale models can be used together to understand different aspects of the same problem.

Crowd analysis is the practice of interpreting data on the natural movement of groups or objects. Masses of bodies, particularly humans, are the subjects of these crowd tracking analyses that include how a particular crowd moves and when a movement pattern changes. Researchers use the data to predict future crowd movement, crowd density, and plan responses to potential events such as those that require evacuation routes. Applications of crowd analysis can range from video game crowd simulation to security and surveillance.

<span class="mw-page-title-main">Erica Kuligowski</span> American social research scientist

Erica Kuligowski is an American social research scientist investigating human behavior during emergencies and the performance of evacuation models in disasters. She currently works at RMIT university in Melbourne (Australia). Kuligowski used to work the Engineering Lab of the National Institute of Standards and Technology conducting research on several fire disasters including the NIST Hurricane Maria Project.

<span class="mw-page-title-main">Michel Bierlaire</span> Belgian-Swiss mathematician

Michel Bierlaire is a Belgian-Swiss applied mathematician specialized in transportation modeling and optimization. He is a professor at EPFL and the head of the Transport and Mobility Laboratory.

References

  1. 1 2 3 Kuligowski, Erica D., Richard D. Peacock, and Bryan L. Hoskins. (2005). A review of building evacuation models. NIST.{{cite book}}: CS1 maint: multiple names: authors list (link)
  2. 1 2 Murray-Tuite, Pamela; Wolshon, Brian (2013-02-01). "Evacuation transportation modeling: An overview of research, development, and practice". Transportation Research Part C: Emerging Technologies. Selected papers from the Seventh Triennial Symposium on Transportation Analysis (TRISTAN VII). 27: 25–45. Bibcode:2013TRPC...27...25M. doi:10.1016/j.trc.2012.11.005. ISSN   0968-090X.
  3. 1 2 3 4 Lovreglio, Ruggiero; Ronchi, Enrico; Kinsey, Michael J. (2020-05-01). "An Online Survey of Pedestrian Evacuation Model Usage and Users". Fire Technology. 56 (3): 1133–1153. doi: 10.1007/s10694-019-00923-8 . ISSN   1572-8099.
  4. 1 2 Kuligowski, Erica D. (2016), Hurley, Morgan J.; Gottuk, Daniel; Hall, John R.; Harada, Kazunori (eds.), "Computer Evacuation Models for Buildings", SFPE Handbook of Fire Protection Engineering, New York, NY: Springer, pp. 2152–2180, doi:10.1007/978-1-4939-2565-0_60, ISBN   978-1-4939-2565-0 , retrieved 2024-10-08
  5. 1 2 Gwynne, Steven M. V.; Rosenbaum, Eric R. (2016), Hurley, Morgan J.; Gottuk, Daniel; Hall, John R.; Harada, Kazunori (eds.), "Employing the Hydraulic Model in Assessing Emergency Movement", SFPE Handbook of Fire Protection Engineering, New York, NY: Springer, pp. 2115–2151, doi:10.1007/978-1-4939-2565-0_59, ISBN   978-1-4939-2565-0 , retrieved 2024-10-08
  6. 1 2 Murray-Tuite, Pamela; Wolshon, Brian (2013-02-01). "Evacuation transportation modeling: An overview of research, development, and practice". Transportation Research Part C: Emerging Technologies. Selected papers from the Seventh Triennial Symposium on Transportation Analysis (TRISTAN VII). 27: 25–45. Bibcode:2013TRPC...27...25M. doi:10.1016/j.trc.2012.11.005. ISSN   0968-090X.
  7. ↑ Lovreglio, Ruggiero; Ronchi, Enrico; Borri, Dino (2014-11-01). "The validation of evacuation simulation models through the analysis of behavioural uncertainty". Reliability Engineering & System Safety. 131: 166–174. doi:10.1016/j.ress.2014.07.007. ISSN   0951-8320.
  8. ↑ Professor Rino (2012-04-15). FDS+Evac evacuation process 2 (Evacuation Modelling) . Retrieved 2024-10-09– via YouTube.
  9. ↑ Francis, R. L. & Saunders, P. B. (1979). EVACNET: Prototype Network Optimization Models for Building Evacuation (PDF) (1 ed.). (Rep. No. NBSIR 79-1593). Natl. Bur. Stand., (U.S.).{{cite book}}: CS1 maint: multiple names: authors list (link)
  10. ↑ Nelson, Harold E. (1990). FPETOOL: Fire Protection Tools for Hazard Estimation (PDF) (1 ed.). NIST.
  11. ↑ Fahy, R. "Exit 89-An Evacuation Model for High-Rise Buildings-Model Description and Example Applications" (PDF). IAFSS Proceedings.
  12. ↑ Gwynne, S.; Galea, E. R.; Owen, M.; Lawrence, P. J.; Filippidis, L. (November 1999). "A review of the methodologies used in evacuation modelling". Fire and Materials. 23 (6): 383–388. doi:10.1002/(SICI)1099-1018(199911/12)23:6<383::AID-FAM715>3.0.CO;2-2. ISSN   0308-0501.
  13. ↑ Vermuyten, Hendrik; Beliën, Jeroen; De Boeck, Liesje; Reniers, Genserik; Wauters, Tony (2016-08-01). "A review of optimisation models for pedestrian evacuation and design problems". Safety Science. 87: 167–178. doi:10.1016/j.ssci.2016.04.001. ISSN   0925-7535.
  14. ↑ Southworth, F (1991-01-01). "Regional Evacuation Modeling: A State of the Art Reviewing". Engineering, Environmental Science: ORNL/TM–11740, 814579. doi:10.2172/814579. S2CID   107348948.
  15. 1 2 Pel, Adam J.; Bliemer, Michiel C. J.; Hoogendoorn, Serge P. (2012-01-01). "A review on travel behaviour modelling in dynamic traffic simulation models for evacuations". Transportation. 39 (1): 97–123. doi: 10.1007/s11116-011-9320-6 . ISSN   1572-9435.
  16. ↑ Ronchi, Enrico; Corbetta, Alessandro; Galea, Edwin R.; Kinateder, Max; Kuligowski, Erica; McGrath, Denise; Pel, Adam; Shiban, Youssef; Thompson, Peter; Toschi, Federico (2019-06-01). "New approaches to evacuation modelling for fire safety engineering applications". Fire Safety Journal. 106: 197–209. Bibcode:2019FirSJ.106..197R. doi:10.1016/j.firesaf.2019.05.002. ISSN   0379-7112.
  17. ↑ Di Gangi, Massimo (2011). "Modeling Evacuation of a Transport System: Application of a Multimodal Mesoscopic Dynamic Traffic Assignment Model". IEEE Transactions on Intelligent Transportation. 12 (4): 1157–1166. doi:10.1109/TITS.2011.2143408.
  18. ↑ Lämmel, Gregor; Grether, Dominik; Nagel, Kai (2010-02-01). "The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations". Transportation Research Part C: Emerging Technologies. Information/Communication Technologies and Travel Behaviour. 18 (1): 84–98. Bibcode:2010TRPC...18...84L. doi:10.1016/j.trc.2009.04.020. ISSN   0968-090X.
  19. ↑ Twarogowska, M.; Goatin, P.; Duvigneau, R. (2014-12-15). "Macroscopic modeling and simulations of room evacuation". Applied Mathematical Modelling. 38 (24): 5781–5795. arXiv: 1308.1770 . doi:10.1016/j.apm.2014.03.027. ISSN   0307-904X.
  20. ↑ Tordeux, Antoine; Lämmel, Gregor; Hänseler, Flurin S.; Steffen, Bernhard (2018-08-01). "A mesoscopic model for large-scale simulation of pedestrian dynamics". Transportation Research Part C: Emerging Technologies. 93: 128–147. Bibcode:2018TRPC...93..128T. doi:10.1016/j.trc.2018.05.021. ISSN   0968-090X.
  21. 1 2 Cuesta, Arturo; Abreu, Orlando; Alvear, Daniel, eds. (2016). Evacuation Modeling Trends. doi:10.1007/978-3-319-20708-7. ISBN   978-3-319-20707-0.
  22. ↑ Lämmel, Gregor; Grether, Dominik; Nagel, Kai (2010-02-01). "The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations". Transportation Research Part C: Emerging Technologies. Information/Communication Technologies and Travel Behaviour. 18 (1): 84–98. Bibcode:2010TRPC...18...84L. doi:10.1016/j.trc.2009.04.020. ISSN   0968-090X.
  23. 1 2 Helbing, Dirk; Molnár, Péter (1995-05-01). "Social force model for pedestrian dynamics". Physical Review E. 51 (5): 4282–4286. arXiv: cond-mat/9805244 . Bibcode:1995PhRvE..51.4282H. doi:10.1103/PhysRevE.51.4282. ISSN   1063-651X. PMID   9963139.
  24. ↑ Kirchner, Ansgar; Schadschneider, Andreas (2002-09-01). "Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics". Physica A: Statistical Mechanics and Its Applications. 312 (1): 260–276. arXiv: cond-mat/0203461 . Bibcode:2002PhyA..312..260K. doi:10.1016/S0378-4371(02)00857-9. ISSN   0378-4371.
  25. ↑ Lovreglio, Ruggiero; Ronchi, Enrico; Nilsson, Daniel (2015-11-15). "Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization". Physica A: Statistical Mechanics and Its Applications. 438: 308–320. Bibcode:2015PhyA..438..308L. doi:10.1016/j.physa.2015.06.040. ISSN   0378-4371.
  26. ↑ Pelechano, Nuria; Malkawi, Ali (2008-05-01). "Evacuation simulation models: Challenges in modeling high rise building evacuation with cellular automata approaches". Automation in Construction. 17 (4): 377–385. doi:10.1016/j.autcon.2007.06.005. ISSN   0926-5805.
  27. ↑ Chraibi, Mohcine; Zhang, Jun (2016). JuPedSim: an open framework for simulating and analyzing the dynamics of pedestrians. Berichte aus dem DLR-Institut für Verkehrssystemtechnik. Braunschweig: Deutsches Zentrum für Luft- und Raumfahrt e. V., Institut für Verkehrssystemtechnik.
  28. ↑ Bayram, Vedat (2016-12-01). "Optimization models for large scale network evacuation planning and management: A literature review". Surveys in Operations Research and Management Science. 21 (2): 63–84. doi:10.1016/j.sorms.2016.11.001. ISSN   1876-7354.
  29. ↑ Cova, Thomas J.; Johnson, Justin P. (2003-08-01). "A network flow model for lane-based evacuation routing". Transportation Research Part A: Policy and Practice. 37 (7): 579–604. Bibcode:2003TRPA...37..579C. doi:10.1016/S0965-8564(03)00007-7. ISSN   0965-8564.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.