Arabian Journal for Science and Engineering

, Volume 44, Issue 4, pp 3289–3304 | Cite as

Analyzing the Crowd Evacuation Pattern of a Large Densely Populated Building

  • Yasser M. Alginahi
  • Mohammed Mudassar
  • Muhammad Nomani Kabir
  • Omar TayanEmail author
Research Article - Computer Engineering and Computer Science


Understanding crowd evacuation behavior is of utmost importance for large buildings in order to achieve efficient crowd monitoring and management. This paper presents the simulation and analysis of crowd evacuation pattern for a large building called Al Masjid An Nabawi, widely known as ‘the Haram,’ in Madinah, Saudi Arabia. Legion Evac software is employed to simulate the crowd evacuation. During simulation, Legion computes various metrics that holistically reflect the crowd evacuation pattern, which captures the crowd evacuation behavior. We analyze the magnitude and temporal variations with respect to the general evacuation patterns (GEP) of the building. The magnitude is analyzed using the t-test, which is a hypothesis testing method. However, the temporal variations are analyzed using cross-correlation analysis. The GEP captures the general crowd evacuation behavior (across all sections) of the building by aggregating the evacuation patterns of each section of the building. The crowd evacuation simulation resulted in an evacuation time of 21 min to evacuate a population of approximately 170,000. The analysis of evacuation patterns shows that the evacuation pattern of different sections of the building differs significantly in magnitude, but has significant temporal similarity with respect to GEP. Finally, insights are derived from the analysis results, which aid in efficient crowd monitoring and management.


Crowd evacuation Simulation Legion Evac Crowd evacuation behavior Cross-correlation analysis Evacuation patterns 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors would like to thank the Deanship of Scientific Research at Taibah University for their support to conduct this research work under research grant no. 434/4317.


  1. 1.
    Haron, F.; Alginahi, Y.M.; Kabir, M.N.; Mohamed, A.I.: Software evaluation for crowd evacuation software evaluation for crowd evacuation-case study: Al-Masjid An-Nabawi. Int. J. Comput. Sci. Issues (IJCSI) 6(2), 128–134 (2012)Google Scholar
  2. 2.
    Sarmady, S.: Modeling and Simulation of Movements and Behaviours in Large Crowd Using Cellular Automata. Master Thesis, Universiti Sains Malaysia (2009)Google Scholar
  3. 3.
    Bouvier, E.: From crowd simulation to airbag deployment: particle systems, a new paradigm of simulation. J. Electron. Imaging 6(1), 94–107 (1997)CrossRefGoogle Scholar
  4. 4.
    Helbing, D.; Farkas, I.; Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)CrossRefGoogle Scholar
  5. 5.
    Qiu, F., Hu, X.: Exploiting spatial-temporal heterogeneity for agent-based simulation of pedestrian crowd behavior. Act. Based Model. Simul. 107–127 (2010)Google Scholar
  6. 6.
    Alginahi, Y.M.; Kabir, M.N.; Mohamed, A.I.: Optimization of high-crowd-density facilities based on discrete event simulation. Malays. J. Comput. Sci. 26(4), 312–29 (2013)Google Scholar
  7. 7.
    Wang, J.H.; Sun, J.H.: Principal aspects regarding to the emergency evacuation of large-scale crowds: a brief review of literatures until 2010. Proc. Eng. 71, 1–6 (2014)CrossRefGoogle Scholar
  8. 8.
    Chunmiao, Y.; Chang, L.; Gang, L.; Peihong, Z.: Safety evacuation in building engineering design by using BuildingExodus. Syst. Eng. Proc. 5, 87–92 (2012)CrossRefGoogle Scholar
  9. 9.
    Nassar, K., Bayyoumi, A.: A simulation study of the effect of mosque design on egress times. In: Proceedings of the Winter Simulation Conference, Berlin, p. 110 (2012)Google Scholar
  10. 10.
    Kiyono, J., Mori, N.: Simulation of emergency evacuation behavior during a disaster by Use of elliptic distinct elements. In: 13th World Conference on Earthquake Engineering, vol. 134, pp. 1–6 (2004)Google Scholar
  11. 11.
    Alighadr, S., Fallahi, A., Kiyono, J., Fitrasha, N.R., Miyajima, M.: Emergency evacuation during a disaster. In: Study Case: TimcheMuzaffariyye–Tabriz BazaaIran, Conference Proceeding of 15 WCEE. Lisbon (2012)Google Scholar
  12. 12.
    Chow, W.K.; Ng, C.M.: Waiting time in emergency evacuation of crowded public transport terminals. Saf. Sci. 46(5), 844–57 (2008)CrossRefGoogle Scholar
  13. 13.
    Zhao, Y.; Li, M.; Lu, X.; Tian, L.; Yu, Z.; Huang, K.; Wang, Y.; Li, T.: Optimal layout design of obstacles for panic evacuation using differential evolution. Physica A 465, 175–94 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Onorati, T.; Malizia, A.; Diaz, P.; Aedo, I.: Modeling ontology on accessible evacuation routes for emergencies. Expert Syst. Appl. 41(16), 7124–7134 (2014)CrossRefGoogle Scholar
  15. 15.
    Han, Y.; Liu, H.: Modified social force model based on information transmission toward crowd evacuation simulation. Physica A 469, 499–509 (2017)CrossRefGoogle Scholar
  16. 16.
    Abdelghany, A.; Abdelghany, K.; Mahmassani, H.; Alhalabi, W.: Modeling framework for optimal evacuation of large-scale crowded pedestrian facilities. Eur. J. Oper. Res. 237(3), 1105–1118 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    He, G.Q.; Yang, Y.; Chen, Z.H.; Gu, C.H.; Pan, Z.G.: A review of behavior mechanisms and crowd evacuation animation in emergency exercises. J. Zhejiang Univ. Sci. C. 14(7), 477–485 (2013)CrossRefGoogle Scholar
  18. 18.
    Wijermans, N.; Conrado, C.; Van Steen, M.; Martella, C.; Li, J.: A landscape of crowd-management support: an integrative approach. Saf. Sci. 86, 142–64 (2016)CrossRefGoogle Scholar
  19. 19.
    Tsiftsis, A.; Georgoudas, I.G.; Sirakoulis, G.C.: Real data evaluation of a crowd supervising system for stadium evacuation and its hardware implementation. IEEE Syst. J. 10(2), 649–660 (2016)CrossRefGoogle Scholar
  20. 20.
    Kim, S.; Guy, S.; Hillesland, K.; Zafar, B.; Gutub, A.; Manocha, D.: Velocity-based modeling of physical interactions in dense crowds. In: The Visual Computer. Springer (2014)Google Scholar
  21. 21.
    Curtis, S., Zafar, B., Gutub, A., Manocha, D.: Right of way: asymmetric agent interactions in crowds. In: The Visual Computer: International Journal of Computer Graphics. Springer, Berlin (2012).
  22. 22.
    Kaysi, I.; Alshalalfah, B.; Shalaby, A.; Sayegh, A.; Sayour, M.; Gutub, A.: Users’ evaluation of rail systems in mass events: case study in Mecca, Saudi Arabia. Transp. Res. Record J. Transp. Res. Board 2350, 111–118 (2013). CrossRefGoogle Scholar
  23. 23.
    Xu P., Cao K.: Review of research on simulation platform based on the crowd evacuation. In: Communications in Computer and Information Science, vol. 761, pp. 324–333. Springer, Singapore (2017)Google Scholar
  24. 24.
    Cassol, V.J., Musse, S.R., Jung, C.R., Badler, N.I.: Crowd simulation. In: Simulating Crowds in Egress Scenarios, pp. 19–45. Springer, Cham (2017)Google Scholar
  25. 25.
    Alvarez, P.; Alonso, V.: Using microsimulation software to model large-scale evacuation scenarios, the case of Sangüesa and the Yesa dam collapse. Saf. Sci. 106, 10–27 (2018)CrossRefGoogle Scholar
  26. 26.
    Alginahi, Y.M.; Kabir, M.N.: Simulation of building evacuation: performance analysis and simplified model. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(8), 10–17 (2016)Google Scholar
  27. 27.
    Yuksel, M.E.: Agent-based evacuation modeling with multiple exits using NeuroEvolution of Augmenting Topologies. Adv. Eng. Inform. 35, 30–55 (2018)CrossRefGoogle Scholar
  28. 28.
    Ronchi, E., Kinsey, M.: Evacuation models of the future: insights from an online survey on user’s experiences and needs. In: Capote, J., Alvear, D. (eds.), Proceedings of the Advanced Research Workshop: Evacuation and Human Behaviour in Emergency Situations, pp. 145–155. Universidad de Cantabria (2011)Google Scholar
  29. 29.
    Bakar, N.A.A.; Majid, M.A.; Ismail, K.A.: An overview of crowd evacuation simulation. Adv. Sci. Lett. 23(11), 11428–11431 (2017)CrossRefGoogle Scholar
  30. 30.
    Chen, L.; Tang, T.-Q.; Huang, H.-J.; Wu, J.-J.; Song., Z.: Modeling pedestrian flow accounting for collision avoidance during evacuation. Simul. Model. Pract. Theory 82, 1–11 (2018)CrossRefGoogle Scholar
  31. 31.
    Liu, H.; Liu, B.; Zhang, H.; Li, L.; Qin, X.; Zhang, G.: Crowd evacuation simulation approach based on navigation knowledge and two-layer control mechanism. Inf. Sci. 436, 247–267 (2018)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Liu, B.; Liu, H.; Zhang, H.; Qin, X.: A social force evacuation model driven by video data. Simul. Model. Pract. Theory 84, 190–203 (2018)CrossRefGoogle Scholar
  33. 33.
    Mendenhall, W.; Beaver, R.; Beaver, B.: Introduction to Probability and Statistics, 13th edn. Cengage Learning, Canada (2008)zbMATHGoogle Scholar
  34. 34.
    Rosenthal, R.; Rosnow, R.L.: Essentials of Behavioral Research: Methods and Data Analysis, 2nd edn. McGraw Hill, New York (1991)Google Scholar
  35. 35.
    Sedgwick, P.: Pearson’s correlation coefficient. Br. Med. J. 345, e4483 (2012). CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.Department of Computer Science, Deanship of Academic ServicesTaibah UniversityMedinaSaudi Arabia
  2. 2.Department of Computer Science, College of Computer Science and EngineeringTaibah UniversityMedinaSaudi Arabia
  3. 3.Faculty of Computer Systems and Software EngineeringUniversiti Malaysia PahangGambang, KuantanMalaysia
  4. 4.Department of Computer Engineering, College of Computer Science and EngineeringTaibah UniversityMedinaSaudi Arabia
  5. 5.IT Research Center for the Holy Quran and Its Sciences (NOOR)Taibah UniversityMedinaSaudi Arabia

Personalised recommendations