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Evacuation model

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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, tsunamis.

A small-scale simulation run on FDS+Evac. The simulation of a classroom[3]

Simulation scale

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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.[4][1][5] One of the last surveys shows that there are 72 small-scale evacuation models currently in use for fire evacuation.[6]

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][7][8]

Simulation resolution

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The simulation resolution in evacuation models refers to the level of detail and granularity used to represent evacuees and their environment during a simulation.[9][10]

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.[11]

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.[12]

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.[13]

The choice of simulation scale is crucial in balancing model complexity, computational cost and the specific goals of the evacuation study.[14]

Movement representation

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Movement representation refers to how the physical movement of evacuees is simulated within a space, influencing the accuracy and realism of the model.[15][16][17]

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.[18][19]

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.[16][20]

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.[21][22]

Each method of movement representation has strengths and is chosen based on the environment's complexity, required accuracy and computational efficiency.[14]

References

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  1. ^ a b 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. ^ a b 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. ^ 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.
  4. ^ 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.
  5. ^ 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.
  6. ^ 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.
  7. ^ 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.
  8. ^ 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.
  9. ^ 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.
  10. ^ 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.
  11. ^ 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. doi:10.1016/j.trc.2009.04.020. ISSN 0968-090X.
  12. ^ Twarogowska, M.; Goatin, P.; Duvigneau, R. (2014-12-15). "Macroscopic modeling and simulations of room evacuation". Applied Mathematical Modelling. 38 (24): 5781–5795. doi:10.1016/j.apm.2014.03.027. ISSN 0307-904X.
  13. ^ 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. doi:10.1016/j.trc.2018.05.021. ISSN 0968-090X.
  14. ^ a b Evacuation Modeling Trends. doi:10.1007/978-3-319-20708-7.
  15. ^ 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.
  16. ^ a b 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.
  17. ^ 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.
  18. ^ 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. doi:10.1016/j.physa.2015.06.040. ISSN 0378-4371.
  19. ^ 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.
  20. ^ 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.
  21. ^ 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.
  22. ^ 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. doi:10.1016/S0965-8564(03)00007-7. ISSN 0965-8564.
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