An Application of Graphics Processing Units to Geosimulation of Collective Crowd Behaviour

Jānis Cjoskāns, Arnis Lektauers


The goal of the paper is to assess the ways for computational performance and efficiency improvement of collective crowd behaviour simulation by using parallel computing methods implemented on graphics processing unit (GPU). To perform an experimental evaluation of benefits of parallel computing, a new GPU-based simulator prototype is proposed and the runtime performance is analysed. Based on practical examples of pedestrian dynamics geosimulation, the obtained performance measurements are compared to several other available multi- agent simulation tools to determine the efficiency of the proposed simulator, as well as to provide generic guidelines for the efficiency improvements of the parallel simulation of collective crowd behaviour.


Crowd behaviour; geosimulation; GPU computing

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“Parallel Programming and Computing Platform, CUDA” [Online]. Available:

“OpenCL - The open standard for parallel programming of heterogeneous systems” [Online]. Available:

C.-L. Su, P.-Y. Chen, C.-C. Lan, L.-S. Huang, and K.-H. Wu, “Overview and comparison of OpenCL and CUDA technology for GPGPU,” 2012 IEEE Asia Pacific Conference on Circuits and Systems, Dec. 2012.

“PTX ISA: Parallel Thread Execution ISA Version 5.0,” 2017. [Online]. Available:

I. Benenson and P. M. Torrens, Geosimulation: Automata-based modelling of Urban Phenomena. Chichester: John Wiley & Sons Ltd, 2004.

M. Smith, P. Longley and M. Goodchild, “Geospatial Analysis: A comprehensive guide,” 2015. [Online]. Available:

W. E. Easterling and K. Kok, “Emergent Properties of Scale in Global Environmental Modeling – Are There Any?,” Integrated Assessment, vol. 3, no. 2–3, pp. 233–246, Jun. 2002.

R. L. Goldstone and M. A. Janssen, “Computational models of collective behavior,” Trends in Cognitive Sciences, vol. 9, no. 9, pp. 424–430, Sep. 2005.

M. Lysenko and R. D’Souza, “A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units,” Journal of Artificial Societies and Social Simulation, vol. 11, no. 4, p. 10, 2008.

“PEDSIM – A Pedestrian Crowd Simulation” [Online]. Available:

D. Helbing and P. Molnár, “Social force model for pedestrian dynamics,” Physical Review E, vol. 51, no. 5, pp. 4282–4286, May 1995.

M. Moussaid, D. Helbing, S. Garnier, A. Johansson, M. Combe and G. Theraulaz, “Experimental study of the behavioural mechanisms underlying self-organization in human crowds,” Proceedings of the Royal Society B: Biological Sciences, vol. 276, no. 1668, pp. 2755–2762, May 2009.

D. P. Ames, K. Asch, N. Bartelme, M. Becker and E. Al, Springer Handbook of Geographic Information, 1st ed. Würzburg: Springer- Verlag Berlin Heidelberg, 2012.

R. Ding, X. Meng and Y. Bai, “Efficient index update for moving objects with future trajectories,” in Proceedings – 8th International Conference on Database Systems for Advanced Applications, DASFAA 2003, 2003, pp. 183–191.

“IEEE Standard for Floating-Point Arithmetic” [Online]. Available:


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