Modelling group dynamics for crowd simulations

Personal Technologies - Tập 26 - Trang 1299-1319 - 2022
R. A. Saeed1, Diego Reforgiato Recupero2, Paolo Remagnino3
1Faculty of Science and Technology, University of Bolzano, Bolzano, Italy
2Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy
3Department of Computer Science, Kingston University, London, UK

Tóm tắt

This paper investigates a new method to simulate pedestrian crowd movement in a large and complex virtual environment, representing a public space such as a shopping mall. To demonstrate pedestrian dynamics, we consider groups of pedestrians of different size, sharing a crowded environment. A pedestrian has its own characteristics, such as gender, age, position, velocity, and energy. The proposed method uses a multi-group microscopic model to generate real-time trajectories for all people moving in the defined virtual environment. Additionally, a dynamic model is introduced for modelling group behaviour. Based on the proposed method, all pedestrians in each group can continuously adjust their attributes and optimize their path towards the desired visiting targets, while avoiding obstacles and other pedestrians. Simulation results show that the proposed method can describe a realistic simulation of dynamic behaviour.

Tài liệu tham khảo

Ali S, Nishino K, Manocha D, Shah M (2013) Modeling, simulation and visual analysis of crowds: a multidisciplinary perspective. In: Modeling, simulation and visual analysis of crowds. Springer, pp 1–19 Guy SJ (2012) Geometric collision avoidance for heterogeneous crowd simulation Narain R, Golas A, Curtis S, Lin MC (2009) Aggregate dynamics for dense crowd simulation. In: ACM SIGGRAPH Asia 2009 papers. pp 1–8 Hughes RL (2003) The flow of human crowds. Annual Review of Fluid Mechanics 35(1):169–182 Izadinia H, Saleemi I, Li W, Shah M (2012) 2 t: multiple people multiple parts tracker. In: European conference on computer vision. Springer, pp 100–114 Lim CK, Tan KL, Zaidan AA, Zaidan BB (2020) A proposed methodology of bringing past life in digital cultural heritage through crowd simulation: a case study in George town. Malaysia. Multimedia Tools and Applications 79(5):3387–3423 Durupinar F, Pelechano N, Allbeck J, Gudukbay U, Badler NI (2009) How the ocean personality model affects the perception of crowds. IEEE Computer Graphics and Applications 31(3):22–31 Guy SJ, Kim S, Lin MC, Manocha D (2011) Simulating heterogeneous crowd behaviors using personality trait theory. In: Proceedings of the 2011 ACM SIGGRAPH/Eurographics symposium on computer animation. pp 43–52 Degond P, Appert-Rolland C, Moussaid M, Pettré J, Theraulaz G (2013) A hierarchy of heuristic-based models of crowd dynamics. Journal of Statistical Physics 152(6):1033–1068 Piccoli B, Tosin A (2009) Pedestrian flows in bounded domains with obstacles. Continuum Mechanics and Thermodynamics 21(2):85–107 Treuille A, Cooper S, Popović Z (2006) Continuum crowds. ACM Transactions on Graphics (TOG) 25(3):1160–1168 Etikyala R, Göttlich S, Klar A, Tiwari S (2014) Particle methods for pedestrian flow models: From microscopic to nonlocal continuum models. Mathematical Models and Methods in Applied Sciences 24(12):2503–2523 Hughes RL (2002) A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological 36(6):507–535 Shao W, Terzopoulos D (2007) Autonomous pedestrians. Graphical Models 69(5–6):246–274 Pelechano N, Allbeck JM, Badler NI (2007) Controlling individual agents in high-density crowd simulation Kim S, Guy SJ, Manocha D, Lin MC (2012) Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory. In: Proceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games. pp 55–62 Sarmady S, Haron F, Talib AZH (2009) Modeling groups of pedestrians in least effort crowd movements using cellular automata. In: 2009 Third Asia international conference on modelling & simulation. IEEE, pp 520–525 Cheng L, Reddy V, Fookes C, Yarlagadda PK (2014) Impact of passenger group dynamics on an airport evacuation process using an agent-based model. In: 2014 international conference on computational science and computational intelligence, vol 2. IEEE, pp 161–167 Manenti L, Manzoni S (2011) Crystals of crowd: Modelling pedestrian groups using mas-based approach. In : WOA. pp 51–57 Mahato NK, Klar A, Tiwari S (2018) Particle methods for multi-group pedestrian flow. Applied Mathematical Modelling 53:447–461 Yang S, Li T, Gong X, Peng B, Hu J (2020) A review on crowd simulation and modeling. Graphical Models 111:101081 Reynolds CW (1999) Steering behaviors for autonomous characters. In: Game developers conference, vol 1999. Citeseer, pp 763–782 Patil S, Van Den Berg J, Curtis S, Lin MC, Manocha D (2010) Directing crowd simulations using navigation fields. IEEE Transactions on Visualization and Computer Graphics 17(2):244–254 Sud A, Andersen E, Curtis S, Lin M, Manocha D (2007) Real-time path planning for virtual agents in dynamic environments. In: 2007 IEEE virtual reality conference. IEEE, pp 91–98 Helbing D, Farkas IJ, Molnar P, Vicsek T (2002) Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian and Evacuation Dynamics 21(2):21–58 Epstein JM (2014) AgentZero: Toward Neurocognitive foundations for generative social sciences. Princeton University Press, Princeton Smaldino PE, Epstein JM (2015) Social conformity despite individual preferences for distinctiveness. Royal Society Open Science 2:140437 Helbing D (1992) A fluid-dynamic model for the movement of pedestrians. Complex Systems 6(5):391–415 Helbing D, Molnar P (1995) Social force model for pedesrtian dynamics. Physical Review E 51(5):4282–4286 Helbing D (2010) Quantitative sociodynamics: Stochastic methods and models of social interaction processes. Springer, New York Autodesk (2018) Maya. https://www.autodesk.com/maya/. Accessed June 2019 Saeed RA, Recupero DR, Remagnino P (2020) A boundary node method for path planning of mobile robots. Robotics and Autonomous Systems 123:103320 Saeed RA, Recupero DR (2019) Path planning of a mobile robot in grid space using boundary node method. Proceedings of the 16th international conference on informatics in control, automation and robotics, ICINCO 2019, vol 2. pp 159–166 Reynolds CW (1987) Flocks, herds and schools: A distributed behavioral model. In: Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp 25–34 Adobe (2018) Mixamo. https://www.mixamo.com/. Accessed June 2019