Abstract
Crowd simulation is becoming the dominant way to design infrastructures where large numbers of people transit or move and to plan mass events. Simulation software range from commercial products provided with extensive documentation to open-source codes available for research and development. The commercialization of crowd simulators has allowed to produce user-friendly software requiring little expertise to be used and generating visually realistic results. However, to correctly set up a simulation scenario involving crowd, it is important to have a basic understanding on how these simulators work and what are their limitations. In addition, the large variety of models and products available to simulate crowds could become a challenge when a selection is required. In this chapter, we explain working principles of crowd simulators while also proposing a methodology to select the best product/solution fitting one’s requirements. Also, we discuss the important topic of validation, proposing methods to judge on the accuracy of a particular simulation. Finally, methods to visualize the results will be discussed and compared to allow users picking up the right method depending on the simulated scenario.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zheng, X., Zhong, T., Liu, M.: Modeling crowd evacuation of a building based on seven methodological approaches. Build. Environ. 44(3), 437–445 (2009). https://doi.org/10.1016/j.buildenv.2008.04.002
Templeton, A., Drury, J., Philippides, A.: From mindless masses to small groups: conceptualizing collective behavior in crowd modeling. Rev. Gener. Psychol. 19(3), 215–229 (2015). https://doi.org/10.1037/gpr0000032
Lovreglio, R., Ronchi, E., Kinsey, M.J.: An online survey of pedestrian evacuation model usage and users. Fire Technol. 1–21 (2019). https://doi.org/10.1007/s10694-019-00923-8
**a, Y., Wong, S., Shu, C.W.: Dynamic continuum pedestrian flow model with memory effect. Phys. Rev. E 79(6),(2009). https://doi.org/10.1103/PhysRevE.79.066113
Guo, R.Y.: Potential-based dynamic pedestrian flow assignment. Transp. Res. Part C Emerg. Technol. 91, 263–275 (2018). https://doi.org/10.1016/j.trc.2018.04.011
Lohner, R., Baqui, M., Haug, E., Muhamad, B.: Real-time micro-modelling of a million pedestrians. Eng. Comput. (2016). https://doi.org/10.1108/EC-02-2015-0036
Makinoshima, F., Imamura, F., Abe, Y.: Enhancing a tsunami evacuation simulation for a multi-scenario analysis using parallel computing. Simul. Modell. Pract. Theor. 83, 36–50 (2018). https://doi.org/10.1016/j.simpat.2017.12.016
Lohner, R., Muhamad, B., Dambalmath, P., Haug, E.: Fundamental diagrams for specific very high density crowds. Collect. Dyn. 2, 1–15 (2018). https://doi.org/10.17815/CD.2017.13
Lopez-Carmona, M.A., Garcia, A.P.: Cellevac: an adaptive guidance system for crowd evacuation through behavioral optimization. Saf. Sci. 139 (2021). https://doi.org/10.1016/j.ssci.2021.105215
Hoogendoorn, S.P., Bovy, P.H.: Pedestrian route-choice and activity scheduling theory and models. Transp. Res. Part B Methodol. 38(2), 169–190 (2004). https://doi.org/10.1016/S0191-2615(03)00007-9
Papadimitriou, E., Yannis, G., Golias, J.: A critical assessment of pedestrian behaviour models. Transp. Res. Part F Traff. Psychol. Behav. 12(3), 242–255 (2009). https://doi.org/10.1016/j.trf.2008.12.004
Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., Seyfried, A.: Evacuation Dynamics: Empirical Results, Modeling and Applications, pp. 517–550. Springer New York (2011). https://doi.org/10.1007/978-1-4419-7695-6_29
Hensher, D.A., Rose, J.M., Rose, J.M., Greene, W.H.: Applied Choice Analysis: A Primer. Cambridge University Press (2005)
Wooldridge, J.M.: Introductory econometrics: a modern approach. Cengage Learning (2015)
de Dios Ortúzar, J., Willumsen, L.G.: Modelling transport. John wiley & sons (2011)
Ben-Akiva, M., Lerman, S.R.: Discrete choice analysis: theory and application to travel demand. Transportation Studies (2018)
Beale, L., Field, K., Briggs, D., Picton, P., Matthews, H.: Map** for wheelchair users: route navigation in urban spaces. Cartograph. J. 43(1), 68–81 (2006). https://doi.org/10.1179/000870406X93517
Church, R.L., Marston, J.R.: Measuring accessibility for people with a disability. Geograph. Anal. 35(1), 83–96 (2003). https://doi.org/10.1111/j.1538-4632.2003.tb01102.x
Ding, D., Parmanto, B., Karimi, H.A., Roongpiboonsopit, D., Pramana, G., Conahan, T., Kasemsuppakorn, P.: Design considerations for a personalized wheelchair navigation system. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4790–4793. IEEE (2007). https://doi.org/10.1109/IEMBS.2007.4353411
Dzafic, D., Link, J.A.B., Baumeister, D., Kowalewski, S., Wehrle, K.: Requirements for dynamic route planning for wheelchair users. In: International Conference on Indoor Positioning and Indoor Navigation, vol. 27, pp. 1–4 (2014)
Kasemsuppakorn, P., Karimi, H.A., Ding, D., Ojeda, M.A.: Understanding route choices for wheelchair navigation. Disabil. Rehabil. Assistive Technol. 10(3), 198–210 (2015). https://doi.org/10.1007/978-3-540-70540-6_164
Neis, P.: Measuring the reliability of wheelchair user route planning based on volunteered geographic information. Trans. GIS 19(2), 188–201 (2015). https://doi.org/10.1111/tgis.12087
Crociani, L., Vizzari, G., Yanagisawa, D., Nishinari, K., Bandini, S.: Route choice in pedestrian simulation: design and evaluation of a model based on empirical observations. Intelligenza Artificiale 10(2), 163–182 (2016). https://doi.org/10.3233/IA-160102
Li, M., Shu, P., **ao, Y., Wang, P.: Modeling detour decision combined the tactical and operational layer based on perceived density. Phys. A Stat. Mech. Appl. 574 (2021). https://doi.org/10.1016/j.physa.2021.126021
Blue, V.J., Adler, J.L.: Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transp. Res. Part B Methodol. 35(3), 293–312 (2001). https://doi.org/10.1016/S0191-2615(99)00052-1
Burstedde, C., Klauck, K., Schadschneider, A., Zittartz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Phys. A Stat. Mech. Appl. 295(3–4), 507–525 (2001). https://doi.org/10.1016/S0378-4371(01)00141-8
Nishinari, K., Kirchner, A., Namazi, A., Schadschneider, A.: Extended floor field ca model for evacuation dynamics. IEICE Trans. Inf. Syst. 87(3), 726–732 (2004)
Li, S., Li, X., Qu, Y., Jia, B.: Block-based floor field model for pedestrian’s walking through corner. Phys. A Stat. Mech. Appl. 432, 337–353 (2015). https://doi.org/10.1016/j.physa.2015.03.041
Suma, Y., Yanagisawa, D., Nishinari, K.: Anticipation effect in pedestrian dynamics: modeling and experiments. Phys. A Stat. Mech. Appl. 391(1–2), 248–263 (2012). https://doi.org/10.1016/j.physa.2011.07.022
Kirchner, A., Nishinari, K., Schadschneider, A.: Friction effects and clogging in a cellular automaton model for pedestrian dynamics. Phys. Rev. E 67(5) (2003). https://doi.org/10.1103/PhysRevE.67.056122
Henein, C.M., White, T.: Macroscopic effects of microscopic forces between agents in crowd models. Phys. A Stat. Mech. Appl. 373, 694–712 (2007). https://doi.org/10.1016/j.physa.2006.06.023
Weng, W., Chen, T., Yuan, H., Fan, W.: Cellular automaton simulation of pedestrian counter flow with different walk velocities. Phys. Rev. E 74(3) (2006). https://doi.org/10.1103/PhysRevE.74.036102
Vizzari, G., Manenti, L., Crociani, L.: Adaptive pedestrian behaviour for the preservation of group cohesion. Complex Adap. Syst. Model. 1(1), 7 (2013). https://doi.org/10.1186/2194-3206-1-7
Feliciani, C., Murakami, H., Shimura, K., Nishinari, K.: Efficiently informing crowds-experiments and simulations on route choice and decision making in pedestrian crowds with wheelchair users. Transp. Res. Part C Emerg. Technol. 114, 484–503 (2020). https://doi.org/10.1016/j.trc.2020.02.019
Nowak, S., Schadschneider, A.: Quantitative analysis of pedestrian counterflow in a cellular automaton model. Phys. Rev. E 85(6) (2012). https://doi.org/10.1103/PhysRevE.85.066128
Feliciani, C., Nishinari, K.: An improved cellular automata model to simulate the behavior of high density crowd and validation by experimental data. Phys. A Stat. Mech. Appl. 451, 135–148 (2016). https://doi.org/10.1016/j.physa.2016.01.057
Zeng, W., Chen, P., Yu, G., Wang, Y.: Specification and calibration of a microscopic model for pedestrian dynamic simulation at signalized intersections: a hybrid approach. Transp. Res. Part C Emerg. Technol. 80, 37–70 (2017). https://doi.org/10.1016/j.trc.2017.04.009
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995). https://doi.org/10.1103/PhysRevE.51.4282
Zanlungo, F., Ikeda, T., Kanda, T.: Social force model with explicit collision prediction. EPL (Europhys. Lett.) 93(6), 68005 (2011) https://doi.org/10.1209/0295-5075/93/68005
Friis, C., Svensson, L.: Pedestrian microsimulation. a comparative study between the software programs vissim and viswalk. Master’s thesis, Chalmers University of Technology (2013)
Heydemans, E., Sumabrata, R.J.: The analysis of pedestrian’s facility level of service at pondok cina rail station’s platform using ptv viswalk. In: MATEC Web of Conferences, vol. 278, p. 05001. EDP Sciences (2019). https://doi.org/10.1051/matecconf/201927805001
Pan, X., Han, C.S., Dauber, K., Law, K.H.: A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. Ai & Soc. 22(2), 113–132 (2007). https://doi.org/10.1007/s00146-007-0126-1
Shi, X., Xue, S., Feliciani, C., Shiwakoti, N., Lin, J., Li, D., Ye, Z.: Verifying the applicability of a pedestrian simulation model to reproduce the effect of exit design on egress flow under normal and emergency conditions. Phys. A Stat. Mech. Appl. 562 (2021). https://doi.org/10.1016/j.physa.2020
Ezaki, T., Nishinari, K.: Potential global jamming transition in aviation networks. Phys. Rev. E 90(2) (2014). https://doi.org/10.1103/PhysRevE.90.022807
Ramezani, M., Haddad, J., Geroliminis, N.: Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control. Transp. Res. Part B Methodol. 74, 1–19 (2015). https://doi.org/10.1016/j.trb.2014.12.010
Karamouzas, I., Skinner, B., Guy, S.J.: Universal power law governing pedestrian interactions. Phys. Rev. Lett. 113(23) (2014). https://doi.org/10.1103/PhysRevLett.113.238701
Guo, R.Y., Wong, S., Huang, H.J., Zhang, P., Lam, W.H.: A microscopic pedestrian-simulation model and its application to intersecting flows. Physica A Stat. Mech. Appl. 389(3), 515–526 (2010). https://doi.org/10.1016/j.physa.2009.10.008
Robin, T., Antonini, G., Bierlaire, M., Cruz, J.: Specification, estimation and validation of a pedestrian walking behavior model. Transp. Res. Part B Methodol. 43(1), 36–56 (2009). https://doi.org/10.1016/j.trb.2008.06.010
Helbing, D.: A fluid dynamic model for the movement of pedestrians. ar**v preprint cond-mat/9805213 (1998). https://arxiv.org/abs/cond-mat/9805213
Hoogendoorn, S., Bovy, P.H.: Gas-kinetic modeling and simulation of pedestrian flows. Transp. Res. Record 1710(1), 28–36 (2000). https://doi.org/10.3141/1710-04
Twarogowska, M., Goatin, P., Duvigneau, R.: Macroscopic modeling and simulations of room evacuation. Appl. Math. Modell. 38(24), 5781–5795 (2014). https://doi.org/10.1016/j.apm.2014.03.027
Kouskoulis, G., Spyropoulou, I., Antoniou, C.: Pedestrian simulation: Theoretical models vs. data driven techniques. Int. J. Transp. Sci. Technol. 7(4), 241–253 (2018). https://doi.org/10.1016/j.ijtst.2018.09.001
Duives, D.C., Wang, G., Kim, J.: Forecasting pedestrian movements using recurrent neural networks: An application of crowd monitoring data. Sensors 19(2), 382 (2019). https://doi.org/10.3390/s19020382
Korhonen, T., Hostikka, S.: Fire dynamics simulator with evacuation: Fds+ evac: Technical reference and user’s guide. Tech. Rep, VTT Technical Research Centre of Finland (2009)
Horni, A., Nagel, K., Axhausen, K.W.: The multi-agent transport simulation MATSim. Ubiquity Press (2016)
Chraibi, M., Zhang, J.: Jupedsim: an open framework for simulating and analyzing the dynamics of pedestrians. In: SUMO Conference 2016, FZJ-2016-02717. Jülich Supercomputing Center (2016)
Zönnchen, B., Kleinmeier, B., Köster, G.: Vadere—a simulation framework to compare locomotion models. In: Traffic and Granular Flow 2019, pp. 331–337. Springer (2020). https://doi.org/10.1007/978-3-030-55973-1_41
Feliciani, C., Murakami, H., Nishinari, K.: A universal function for capacity of bidirectional pedestrian streams: filling the gaps in the literature. PloS one 13(12) (2018). https://doi.org/10.1371/journal.pone.0208496
Boltes, M., Holl, S., Seyfried, A.: Data archive for exploring pedestrian dynamics and its application in dimensioning of facilities for multidirectional streams. Collect. Dyn. 5, 17–24 (2020) https://doi.org/10.17815/CD.2020.28
Murakami, H., Feliciani, C., Nishiyama, Y., Nishinari, K.: Mutual anticipation can contribute to self-organization in human crowds. Sci. Adv. 7(12), eabe7758 (2021). https://doi.org/10.1126/sciadv.abe7758
Duives, D.C., Daamen, W., Hoogendoorn, S.P.: State-of-the-art crowd motion simulation models. Transp. Res. Part C Emerg. Technol. 37, 193–209 (2013). https://doi.org/10.1016/j.trc.2013.02.005
Kinsey, M., Gwynne, S., Kinateder, M.: Evacuation modelling biases—research, development, and application. In: Fire and Evacuation Modeling Technical Conference (FEMTC), pp. 1–11 (2020)
Boltes, M., Seyfried, A., Steffen, B., Schadschneider, A.: Automatic extraction of pedestrian trajectories from video recordings. In: Pedestrian and Evacuation Dynamics 2008, pp. 43–54. Springer (2010). https://doi.org/10.1007/978-3-642-04504-2_3
Boltes, M., Seyfried, A.: Collecting pedestrian trajectories. Neurocomputing 100, 127–133 (2013). https://doi.org/10.1016/j.neucom.2012.01.036
Helbing, D., Johansson, A., Al-Abideen, H.Z.: Dynamics of crowd disasters: an empirical study. Phys. Rev. E 75(4) (2007). https://doi.org/10.1103/PhysRevE.75.046109
Feliciani, C., Nishinari, K.: Measurement of congestion and intrinsic risk in pedestrian crowds. Transp. Res. Part C Emerg. Technol. 91, 124–155 (2018). https://doi.org/10.1016/j.trc.2018.03.027
Feliciani, C., Zuriguel, I., Garcimartín, A., Maza, D., Nishinari, K.: Systematic experimental investigation of the obstacle effect during non-competitive and extremely competitive evacuations. Sci. Rep. 10(1), 1–20 (2020). https://doi.org/10.1038/s41598-020-72733-w
Hosseini, O., Maghrebi, M., Maghrebi, M.F.: Determining optimum staged-evacuation schedule considering total evacuation time, congestion severity and fire threats. Saf. Sci. 139 (2021). https://doi.org/10.1016/j.ssci.2021.105211
Zanlungo, F., Feliciani, C., Yucel, Z., Nishinari, K., Kanda, T.: A pure number to assess congestion in pedestrian crowds
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Feliciani, C., Shimura, K., Nishinari, K. (2021). Crowd Simulators: Computational Methods, Product Selection, and Visualization. In: Introduction to Crowd Management. Springer, Cham. https://doi.org/10.1007/978-3-030-90012-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-90012-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90011-3
Online ISBN: 978-3-030-90012-0
eBook Packages: Social SciencesSocial Sciences (R0)