LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: Springer US
Languages: English
Types: Article
Subjects:
Evacuation simulation has the potential to be used as part of a decision support system during large-scale incidents to provide advice to incident commanders. To be viable in these applications, it is essential that the simulation can run many times faster than real time. Parallel processing is a method of reducing run times for very large computational simulations by distributing the workload amongst a number of processors. This paper presents the development of a parallel version of the rule based evacuation simulation software buildingEXODUS using domain decomposition. Four Case Studies (CS) were tested using a cluster, consisting of 10 Intel Core 2 Duo (dual core) 3.16 GHz CPUs. CS-1 involved an idealised large geometry, with 20 exits, intended to illustrate the peak computational speed up performance of the parallel implementation, the population consisted of 100,000 agents; the peak computational speedup (PCS) was 14.6 and the peak real-time speedup (PRTS) was 4.0. CS-2 was a long area with a single exit area with a population of 100,000 agents; the PCS was 13.2 and the PRTS was 17.2. CS-3 was a 50 storey high rise building with a population of 8000/16,000 agents; the PCS was 2.48/4.49 and the PRTS was 17.9/12.9. CS-4 is a large realistic urban area with 60,000/120,000 agents; the PCS was 5.3/6.89 and the PRTS was 5.31/3.0. This type of computational performance opens evacuation simulation to a range of new innovative application areas such as real-time incident support, dynamic signage in smart buildings and virtual training environments.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 3. Qureshi WS, Ekpanyapong M, Dailey MN, Rinsurongkawong S, Malenichev A, Krasotkina O (2016) QuickBlaze: early fire detection using a combined video processing approach. Fire Technol 52:1293. doi:10.1007/s10694-015-0489-7
    • 4. Beji T, Verstockt S, Van de Walle R, Merci B (2014) On the use of real-time video to forecast fire growth in enclosures. fire technol 50:1021. doi:10.1007/s10694-012-0262-0
    • 5. Miller-Hooks E, Krauthammer T (2007) An intelligent evacuation, rescue and recovery concept. Fire Technol 43:107. doi:10.1007/s10694-006-8433-5
    • 6. Cowlard A, Jahn W, Abecassis-Empis C, Rein G, Torero JL (2010) Sensor assisted fire fighting. Fire Technol 46:719. doi:10.1007/s10694-008-0069-1
    • 7. Han L, Potter S, Beckett G et al (2010) FireGrid: An e-infrastructure for next-generation emergency response support. J Parallel Distrib Comput 70(11):1128-1141. doi: 10.1016/j.jpdc.2010.06.005. ISSN 0743-7315
    • 8. Galea ER, Xie H, Lawrence P (2016) Intelligent active dynamic signage system: bringing the humble emergency exit sign into the 21st century, SFPE Europe, Q1, Issue 3, 2016. http://www.sfpe.org/general/custom.asp?page=Issue3Feature1
    • 9. Galea ER, Galparsoro JMP (1994) A computer based simulation model for the prediction of evacuation from mass transport vehicles. Fire Saf J 22:341-366. doi:10.1016/ 0379-7112(94)90040-X
    • 10. Gwynne S, Galea ER, Lawrence PJ, Filippidis L (2001) Modelling occupant interaction with fire conditions using the buildingEXODUS evacuation model. Fire Saf J 36:327- 357. doi:10.1016/S0379-7112(00)00060-6
    • 11. Gwynne S, Galea ER, Lawrence PJ (2006) The introduction of social adaptation within evacuation modelling. Fire Mater 30(4):285-309. doi:10.1002/fam.913
    • 12. Galea ER, Sharp G, Lawrence PJ, Holden R (2008) Approximating the evacuation of the world trade center north tower using computer simulation. J Fire Prot Eng 18(2):85-115. doi:10.1177/1042391507079343
    • 13. Helbing D, Mukerji P (2012) Crowd disasters as systemic failures: analysis of the Love Parade disaster. EPJ Data Sci 1(7):1-40. doi:10.1140/epjds7
    • 14. Pretorius M, Gwynne S, Galea E (2012) The collection and analysis of data from a fatal large-scale crowd incident. In: Proceedings of the 5th international symposium, human behaviour in Fire 2012, Cambridge UK, 19-21 Sept 2012, Interscience Communications Ltd., pp 263-274. ISBN: 978-0-9556548-8-6, 2012
    • 15. Helbing D, Johansson A, Al-Abideen HZ (2007) The dynamics of crowd disasters: an empirical study. Phys Rev. doi:10.1103/PhysRevE.75.046109
    • 16. Gwynne SMV, Siddiqui AA (2013) Understanding and simulating large crowds. In: Traffic and granular flow '11, 2013, conference proceedings. Springer-Verlag Berlin Heidelberg, Berlin Heidelberg, Germany, pp. 217-239. ISBN: 9783642396687
    • 17. Benedictus L (2015) Hajj crush: how crowd disasters happen, and how they can be avoided. The Guardian, 3 Oct 2015. https://www.theguardian.com/world/2015/oct/03/ hajj-crush-how-crowd-disasters-happen-and-how-they-can-be-avoided. Accessed 3 Jan 2017
    • 18. Lovreglio R, Ronchi E, Maragkos G, Beji T, Merci B (2016) A dynamic approach for the impact of a toxic gas dispersion hazard considering human behaviour and dispersion modelling. J Hazard Mater 318:758-771. doi: 10.1016/j.jhazmat.2016.06.015. ISSN 0304-3894
    • 19. Chooramun N, Lawrence PJ, Galea ER (2012) An agent based evacuation model utilising hybrid space discretisation. Saf Sci 50:1685-1694. doi:10.1016/j.ssci.2011.12.022
    • 20. Hoogendoorn SP, Bovy PHL (2002) Normative pedestrian behaviour theory and modelling. In: Proceedings of the 15th international symposium on transportation and traffic theory, 2002
    • 21. Lovreglio R (2016) Modelling decision-making in fire evacuation using the random utility theory. Ph.D. thesis, Politecnico di Bari, Milan and Turin (Italy)
    • 22. Vassalos D, Kim H, Christiansen G, Majumder J (2001) A mesoscopic model for passenger evacuation in a virtual ship-sea environment and performance-based evaluation', pedestrian and evacuation dynamics- 4-6 Apr 2001, Duisburg, pp 369-391. ISBN: 3- 540-42690-6
    • 23. Kostreva MM, Lancaster LC (1998) A comparison of two methodologies in HAZARD I fire egress analysis. Fire Technol 34:227. doi:10.1023/A:1015345923210
    • 24. Kirchner A, Klu¨ pfel H, Nishinari K, Schadschneider A, Schreckenberg M (2002) Simulation of competitive egress behaviour. Phys A 324:689-697. doi:10.1016/S0378-4371 (03)00076-1
    • 25. Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Phys Rev E 5:4282-4286. doi:10.1103/PhysRevE.51.4282
    • 26. Chraibi M, Seyfried A, Schadschneider A (2010) Generalized centrifugal-force model for pedestrian dynamics. Phys Rev E 82(4 pt 2):046111. doi:10.1103/PhysRevE.82. 046111
    • 27. Gao Y, Chen T, Luh PB, Zhang H (2016) Modified social force model based on predictive collision avoidance considering degree of competitiveness. Fire Technol . doi:10.1007/s10694-016-0573-7
    • 28. Waterson NP, Mecca A, Wall JM (2004) Evacuation of a multilevel office building: comparison of predicted results using an agent-based model with measured data. In: Interflam 2004: 10th international fire science and engineering conference, Edinburgh, UK, 2004, pp 767-772
    • 29. Fang ZM, Song WG, Zhang J, Wu H (2012) A multi-grid model for evacuation coupling with the effects of fire products. Fire Technol 48:91. doi:10.1007/s10694-010-0173-x
    • 30. Zia K, Farrahi K, Riener A, Ferscha A (2013) An agent-based parallel geo-simulation of urban mobility during city-scale evacuation. Simulation: transactions of the society for modeling and simulation international, May 2013, pp 1-31. doi:10.1177/ 0037549713485468
    • 31. Duncan R (1990) A survey of parallel computer architectures. Computer 23(2):5-16. doi:10.1109/2.44900
    • 32. Giitsidis T, Dourvas NI, Sirakoulis GC (2015) Parallel implementation of aircraft disembarking and emergency evacuation based on cellular automata. Int J High Perform Comput Appl. doi:10.1177/1094342015584533
    • 33. Chen D, Wang L, Zomaya AY, Dou M, Chen J, Deng Z, Hariri S (2015) Parallel simulation of complex evacuation scenarios with adaptive agent models. IEEE Trans Parallel Distrib Syst 26(3):847-857. doi:10.1109/TPDS.2014.2311805
    • 34. Quinn MJ, Metoyer RA, Hunter-Zaworski K (2003) Parallel implementation of the social forces model. In Galea ER (ed) Proceedings of 2nd international pedestrian and evacuation dynamics conference. CMS Press, Greenwich, UK, pp 63-74. ISBN: 1904521088
    • 35. Steffen B, Kemloh U, Chraibi M, Seyfried A (2011) Parallel real time computation of large scale pedestrian evacuations. In: Iva´ nyi P, Topping (BHV) (eds) Proceedings of the second international conference on parallel, distributed, grid and cloud computing for engineering, Civil-Comp Press, Stirlingshire, UK, paper 95. doi:10.4203/ccp.95.95
    • 36. Dagum L, Menon R (1998) OpenMP: an industry standard API for shared-memory programming. IEEE Computat Sci Eng 5(1):46-55. doi:10.1109/99.660313
    • 37. Galea ER, Ierotheou C (1992) Fire field modelling on parallel computers. Fire Saf J 19(4):251-266. doi:10.1016/0379-7112(92)90008-Z
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article