Abstract
Evacuation plans have been historically used as a safety measure for the construction of buildings. The existing crowd simulators require fully modeled 3D environments and enough time to prepare and simulate scenarios, where the distribution and behavior of the crowd needs to be controlled. In addition, its population, routes or even doors and passages may change, so the 3D model and configurations have to be updated accordingly. This is a time-consuming task that commonly has to be addressed within the crowd simulators. With that in mind, we present a novel approach to estimate the resulting data of a given evacuation scenario without actually simulating it. For such, we divide the environment into smaller modular rooms with different configurations, in a divide-and-conquer fashion. Next, we train an artificial neural network to estimate all required data regarding the evacuation of a single room. After collecting the estimated data from each room, we develop a heuristic capable of aggregating per-room information so the full environment can be properly evaluated. Our method presents an average error of \(5\%\) when compared to evacuation time in a real-life environment. Our crowd estimator approach has several advantages, such as not requiring to model the 3D environment, nor learning how to use and configure a crowd simulator, which means any user can easily use it. Furthermore, the computational time to estimate evacuation data (inference time) is virtually zero, which is much better even when compared to the best-case scenario in a real-time crowd simulator.
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References
Aik, L.E., Choon, T.W.: Simulating evacuations with obstacles using a modified dynamic cellular automata mode. J. Appl. Math. (2012). https://doi.org/10.1155/2012/765270
Cassol, V., Oliveira, J., Musse, S.R., Badler, N.: Analyzing egress accuracy through the study of virtual and real crowds. In: 2016 IEEE Virtual Humans and Crowds for Immersive Environments (VHCIE), pp. 1–6 (2016). https://doi.org/10.1109/VHCIE.2016.7563565
Cassol, V., Testa, E., Jung, C., Usman, M., Faloutsos, P., Berseth, G., Kapadia, M., Badler, N., Musse, S.R.: Evaluating and optimizing evacuation plans for crowd egress. IEEE Comput. Graph. Appl. 37(4), 60–71 (2017)
Chan, A.B., Liang, Z.S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE, pp. 1–7 (2008)
Chu, M.L., Parigi, P., Law, K., Latombe, J.C.: Modeling social behaviors in an evacuation simulator. Comput. Anim. Virtual Worlds 25(3–4), 373–382 (2014). https://doi.org/10.1002/cav.1595
Fradi, H., Dugelay, J.L.: Crowd density map estimation based on feature tracks. In: 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP). IEEE, pp. 040–045 (2013)
Fruin, J.: Pedestrian Planning and Design. Metropolitan Association of Urban Designers and Environmental Planners (1971)
Fruin, J.J.: Designing for pedestrians: a level of service concept. Highw. Res. Rec. 355, 1–15 (1971)
Fu, L., Song, W., Lv, W., Lo, S.: Simulation of exit selection behavior using least effort algorithm. Transp. Res. Proc. 2(0), 533–540 (2014). https://doi.org/10.1016/j.trpro.2014.09.093. The Conference on Pedestrian and Evacuation Dynamics: (PED 2014), 22–24 October 2014. Delft, The Netherlands (2014)
Galea, E.R.: A general approach to validating evacuation models with an application to EXODUS. J. Fire Sci. 16(6), 414–436 (1998)
Garrett, A., Carnahan, B., Muhdi, R., Davis, J., Dozier, G., SanSoucie, M.P., Hull, P.V., Tinker, M.L.: Evacuation planning via evolutionary computation. In: IEEE Congress on Evolutionary Computation, 2006. CEC 2006. IEEE, pp. 157–164 (2006)
Gwynne, S., Galea, E.R., Owen, M., Lawrence, P.J., Filippidis, L.: A review of the methodologies used in the computer simulation of evacuation from the built environment. Build. Environ. 34(6), 741–749 (1999)
Hansen, N.: A CMA-ES for Mixed-Integer Nonlinear Optimization. Tech. Rep. RR-7751, INRIA (2011)
Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: IEEE International Conference on Evolutionary Computation, pp. 312–317 (1996)
Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp. Sci. 39(1), 1–24 (2005). https://doi.org/10.1287/trsc.1040.0108
Helbing, D., Keltsch, J., Molnar, P.: Modelling the evolution of human trail systems. Nature 388(6637), 47–50 (1997)
Henderson, L.: The statistics of crowd fluids. Nature 229, 381–383 (1971)
Henderson, L., Lyons, D.: Sexual differences in human crowd motion. Nature 240(5380), 353 (1972)
Henderson, L.F.: On the fluid mechanics of human crowd motion. Transp. Res. 8(6), 509–515 (1974)
Huang, P., Kang, J., Kider, J.T., Sunshine-Hill, B., McCaffrey, J.B., Rios, D.V., Badler, N.I.: Real-time evacuation simulation in mine interior model of smoke and action. In: The 23rd Annual Conference on Computer Animation and Social Agents (CASA), pp. 312–317 (2010)
Ji, L., Qian, Y., Zeng, J., Wang, M., Xu, D., Yan, Y., Feng, S.: Simulation of evacuation characteristics using a 2-dimensional cellular automata model for pedestrian dynamics. J. Appl. Math. (2013). https://doi.org/10.1155/2013/284721
Liu, W., Hu, K., Yoon, S., Pavlovic, V., Faloutsos, P., Kapadia, M.: Characterizing the relationship between environment layout and crowd movement using machine learning (2017)
Ma, W., Yarlagadda, P.K.: Pedestrian dynamics in real and simulated world. J. Urban Plan. Dev. 141(3), 04014030 (2014)
Musse, S.R., Cassol, V.J., Jung, C.R.: Towards a quantitative approach for comparing crowds. Comput. Animat. Virtual Worlds 23(1), 49–57 (2012). https://doi.org/10.1002/cav.1423
Nasir, F.M., Sunar, M.S.: A survey on simulating real-time crowd simulation. In: 2015 4th International Conference on Interactive Digital Media (ICIDM). IEEE, pp. 1–5 (2015)
Pauls, J.L., Jones, B.K.: Building evacuation: research methods and case studies. In: Canter, D. (ed.) Fires and Human Behaviour, pp. 227–249. Wiley, New York (1980)
Polus, A., Schofer, J.L., Ushpiz, A.: Pedestrian flow and level of service. J. Transp. Eng. 109(1), 46–56 (1983)
Schadschneider, A., Seyfried, A.: Empirical results for pedestrian dynamics and their implications for modeling. Netw. Heterog. Med. 6, 545–560 (2011)
Still, G.K.: Crowd dynamics. Ph.D. thesis, University of Warwick, Coventry, UK (2000)
Thalmann, D., Musse, S.R.: Crowd rendering. In: Crowd Simulation, pp. 195–227. Springer, London (2013)
Tilley, A.R.: The Measure of Man and Woman: Human Factors in Design, vol. 1. Wiley, New York (2002)
Tripathi, G., Singh, K., Vishwakarma, D.K.: Convolutional neural networks for crowd behaviour analysis: a survey. Vis. Comput. (2018). https://doi.org/10.1007/s00371-018-1499-5
Van Den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Optimal reciprocal collision avoidance. http://gamma.cs.unc.edu/ORCA/. Accessed 8 May 2019
van den Berg, J.P., Guy, S.J., Lin, M.C., Manocha, D.: Reciprocal n-body collision avoidance. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Robotics Research. Springer Tracts in Advanced Robotics, vol. 70, pp. 3–19. Springer, Berlin, Heidelberg (2011)
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This work was partially funded by Brazilian Research Agency CNPq (Grant Number: 305084/2016-0).
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Testa, E., Barros, R.C. & Musse, S.R. CrowdEst: a method for estimating (and not simulating) crowd evacuation parameters in generic environments. Vis Comput 35, 1119–1130 (2019). https://doi.org/10.1007/s00371-019-01684-9
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DOI: https://doi.org/10.1007/s00371-019-01684-9