• RABIAA CHIGHOUB Department of Computer Sciences, Lesia Laboratory, Biskra University Biskra, Algeria
  • FOUDIL CHERIF Department of Computer Sciences, Lesia Laboratory, Biskra University Biskra, Algeria


In this paper, we addresses the development of agent-based model for real-time simulation of large scale pedestrian crowds.
Its focus is to produce realistic pedestrian navigation and path planning within the environment whilst maintaining real time frame rates.
The main assumption of this work is that the navigational behaviors of pedestrians are modeled realistically through hierarchical motions in multi layers of path planning, local path determination and locomotion. The inter-relationship between these layers is defined.
Our method can be easily combined with most current local collision-avoidance methods and we use two such methods as examples to highlight the potential of our approach. .
We also demonstrate some simulation results of Guarder to show that it could efficiently simulate life like crowd behaviors in a large-scale and complex environment.


[1] Abdelghany, A., Abdelghany, K., Mahmassani, H., Al-Ahmadi, H., & Alhalabi, W. (2010). Modeling the evacuation of large-scale crowded pedestrian facilities. Transportation Research Record: Journal of the
(a) Virtual entity continues to follow their path to destination
virtual group at its goal
(b) Virtual entity achieves its goal
group A
pedestrian B
(a) detection of dynamic collision between 2 entities
(c) Calculate new directions and speeds for each entity Transportation Research Board, 2198, pp. 152–160.
[2] Alizadeh, R. (2011). A dynamic cellular automaton model for evacuation process with obstacles. Safety Science, 49(2), pp. 315–323.
[3] Chighoub, R., & Cherif, F. (2016). Toward a Hybrid Approach for Crowd Simulation. International Journal of Advanced Computer Science and Applications, 7(1), 51-61.
[4] Durupinar, F., Allbeck, J., Pelechano, N., & Badler, N. (2008): Creating crowd variation with the OCEAN personality model. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (May 12-16, 2008)
[5] Guo R Y., & Huang H. J. (2008). A mobile lattice gas model for simulating pedestrian evacuation. Physica A; 387: pp. 580–6.
[6] Guy S, Chuggani J, Curtis S, Dubey P, Lin M, & Manocha D., (2010), Pledestrians: A least-effort approach to crowd simulation. Technical report. Department of Computer Science, University of North Carolina,
[7] Helbing D, Farkas I, & Vicsek T., (2000). Simulating dynamical features of escape panic. Nature; 407: pp. 487–490.
[8] Hughes, R.L., (2002): A continuum theory for the flow of pedestrians. Transp. Res. Part B Methodol. 36(29), pp. 507–535
[9] Huang, L., Wong, S., Zhang, M., Shu, C., W., & Lam, W. H., (2009), Revisiting Hughes’ dynamic continuum model for pedestrian flow and the development of an efficient solution algorithm, Transportation Research Part B: Methodological 43 (1): pp. 127–141
[10] Jiang, H., Xu, W., Mao, T., Li, C., Xia, S., & Wang, Z., (2009): Continuum Crowd Simulation in Complex Environments. Computers & Graphics 2010 34(5), pp. 537–544
[11] Kaminka G-A, & Fridman N., (2006), A cognitive model of crowd behavior based on social comparison theory. Proceedings of the AAAI-2006 Workshop on Cognitive Modeling, July.
[12] Kirchner, A., & Shadschneider, A., (2002): Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Physica A 312(1–2), pp. 260–276
[13] Musse, S. R., & Thalmann, D., (2001). Hierarchical model for real time simulation of virtual human crowds, IEEE Trans. Visualization Comput. Graph. 7, pp. 152–164.
[14] Narain R, Golas A, Curtis S, & Lin M C., (2009), Aggregate dynamics for dense crowd simulation. In: ACM Transactions on Graphics (Proc. of ACM SIGGRAPH Asia)
[15] Pelechano, N., Allbeck, J., & Badler, N., I., (2007): Controlling individual agents in high-density crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation Eurographics Association, pp. 108–118.
[16] Pelechano, N., Allbeck, J., M., & Badler, N., I., (2008), Virtual Crowds: Methods, Simulation and Control. Morgan and Claypool Publishers,
[17] Reynolds, C.W. (1987). Flocks, herds, and schools: A distributed behavioural model. In: Computer Graphics Proceedings of SIGGRAPH 1897, vol. 21, pp. 25–34.
[18] Reynolds, C. (1999). Steering behaviours for autonomous characters. In: Game Developers Conference, pp. 763–782.
[19] Thalmann, D., & Musse, S.R. (2007), Crowd Simulation, 1st edn. Springer
[20] Treuille, A., Cooper, S., Popovic, Z., (2006): Continuum crowds. In: SIGGRAPH 2006: ACM SIGGRAPH 2006 Papers, ACM, New York pp. 1160–1168.
[21] Ulicny, B., & Thalmann, D., (2001). Crowd simulation for interactive virtual environments and VR training systems, Comput. Anim. Simul., pp. 163–170.
[22] Xiong, M., Tang, S., & Zhao, D., (2014). A hybrid model for simulating crowd evacuation. New generation Computing, 31, pp. 211–235.
[23] Xu, M.,L., Jiang, H., Jin, X., G., & Zhigang Deng, Z., (2014). Crowd simulation and its applications: Recent advances. Journal Of Computer Science And Technology 29(5): pp. 799-81. DOI 10.1007/s11390-014-1469-y
[24] Xiong, M., Tang, S., & Zhao, D., (2014). A hybrid model for simulating crowd evacuation. New generation Computing, 31, pp. 211–235.
[25] Zhao, H., & Gao, Z. (2010). Reserve capacity and exit choosing in pedestrian evacuation dynamics. Physics A: Mathematical and Theoretical, 43(10), 105001.
[26] Zheng X, Zhong T, & Liu M., (2009), Modeling crowd evacuation of a building based on seven methodological approaches. Building and Environment; 44(3): pp. 437–445.
Comment citer
CHIGHOUB, RABIAA; CHERIF, FOUDIL. AGENT-BASED MODEL FOR MICROSIMULATION OF LARGE SCAL PEDESTRIAN CROWD. Courrier du Savoir, [S.l.], v. 22, fév. 2017. ISSN 1112-3338. Disponible à l'adresse : >>. Date de consultation : 10 jui. 2020