Advanced Search
Article Contents
Article Contents

A kinetic theory approach to model pedestrian dynamics in bounded domains with obstacles

This work was partially supported by NSF grant DMS-1620384

Abstract Full Text(HTML) Figure(18) Related Papers Cited by
  • We consider a kinetic theory approach to model the evacuation of a crowd from bounded domains. The interactions of a person with other pedestrians and the environment, which includes walls, exits, and obstacles, are modeled by using tools of game theory and are transferred to the crowd dynamics. The model allows to weight between two competing behaviors: the search for less congested areas and the tendency to follow the stream unconsciously in a panic situation. For the numerical approximation of the solution to our model, we apply an operator splitting scheme which breaks the problem into two pure advection problems and a problem involving the interactions. We compare our numerical results against the data reported in a recent empirical study on evacuation from a room with two exits. For medium and medium-to-large groups of people we achieve good agreement between the computed average people density and flow rate and the respective measured quantities. Through a series of numerical tests we also show that our approach is capable of handling evacuation from a room with one or more exits with variable size, with and without obstacles, and can reproduce lane formation in bidirectional flow in a corridor.

    Mathematics Subject Classification: Primary: 35Q91, 91C99; Secondary: 65C20, 65M06.


    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  (A) Dependence of the dimensionless velocity modulus $ v $ on the dimensionless density $ \rho $ for different values of the parameter $ \alpha $, which represents the quality of the environment. (B) Sketch of computational domain $ \Omega $ with exit $ E $ and a pedestrian located at $ {\boldsymbol{x}} $, moving with direction $ \theta_h $. The pedestrian should choose direction $ {\boldsymbol u}_E $ to reach the exit, while direction $ {\boldsymbol u}_W $ is to avoid collision with the wall. The distances form the exit and from the wall are $ d_E $ and $ d_W $, respectively

    Figure 2.  Definition of $ {\boldsymbol u}_W $ and $ {\boldsymbol u}_E $ with respect to the effective area

    Figure 3.  Evacuation process of 46 pedestrians grouped into two clusters with opposite initial directions $ \theta_3 $ and $ \theta_7 $ using the medium mesh and $ \Delta t_{medium} $ for time $ t = 0, 1.5, 3, 6, 10.5, 13.5 $ s. The color refers to density

    Figure 4.  (A) Number of pedestrians inside the room over time computed with six different combinations of mesh and time step. For ease of comparison, (B) shows only the curves in (A) obtained with simultaneous refinements of mesh and time step

    Figure 5.  Computed evacuation time from the room with one exit versus the exit size: (A) our results and (B) results from [1]

    Figure 6.  Computational domain corresponding to the experimental set-up in [40] and initial density and direction (i.e., $ \theta_1 $) for the experiment with 138 pedestrians

    Figure 7.  Computed (A) mean density $ D_{V} $ and (B) mean flow rate $ F_{V} $ as defined in (15), and measured (C) mean density and (D) mean flow rate from [40]

    Figure 8.  (A) Different velocity moduli under consideration and (B) corresponding number of pedestrians in the room versus time for the 138 pedestrian case

    Figure 9.  Density (top) and velocity magnitude with selected velocity vectors (bottom) for the evacuation process of 138 pedestrians with the purple (left), orange (middle), and blue (right) velocity moduli at time $ t = 15 $ s

    Figure 10.  (A) Widths of exit 2 under consideration and corresponding width ratios and (B) evacuation time versus width ratios for two different scenarios

    Figure 11.  Computational domain with effective area for (A) an obstacle placed in the middle of the room, towards the exit, and (B) two obstacles placed symmetrically with respect to the exit

    Figure 12.  Configuration 1 with $ \alpha = 1 $ in the effective area: computed density for $ t $ = 0, 3, 6, 7.5, 9, 13.5 s. The small square within the effective area represents the real obstacle

    Figure 13.  Configuration 1 with $ \alpha = 0 $ in the effective area: computed density for $ t $ = 0, 3, 6, 7.5, 9, 13.5 s. The small rectangle within the effective area represents the real obstacle

    Figure 14.  Configuration 2 with $ \alpha = 1 $ in the effective area: computed density for $ t $ = 0, 3, 6, 7.5, 10.5, 13.5 s. The small square within the effective area represents the real obstacle

    Figure 15.  Configuration 2 with $ \alpha = 0 $ in the effective area: computed density for $ t $ = 0, 3, 6, 7.5, 10.5, 13.5 s s. The small rectangle within the effective area represents the real obstacle

    Figure 16.  Evacuation times for the room with no obstacles ($ \alpha = 1 $ everywhere in the domain), for room with one and two obstacles with $ \alpha = 1 $ and $ \alpha = 0 $ in the effective area

    Figure 17.  The movement process of 98 pedestrians grouped into four clusters with opposite initial direction $ \theta_1 $ and $ \theta_5 $ in the periodic corridor for $ t = 0, 4.2, 12.3, 19.8, 33.9, 50.7, 72.3 $ s, respectively

    Figure 18.  The movement process of 188 pedestrians grouped into four clusters with initial opposite direction $ \theta_1 $ and $ \theta_5 $ in the periodic corridor for $ t = 0, 4.5, 12.6, 19.8, 33.9, 50.7, 89.7 $ s, respectively

  • [1] J. P. AgnelliF. Colasuonno and D. Knopoff, A kinetic theory approach to the dynamics of crowd evacuation from bounded domains, Mathematical Models and Methods in Applied Sciences, 25 (2015), 109-129.  doi: 10.1142/S0218202515500049.
    [2] G. AntoniniM. Bierlaire and M. Weber, Discrete choice models of pedestrian walking behavior, Transportation Research Part B: Methodological, 40 (2006), 667-687.  doi: 10.1016/j.trb.2005.09.006.
    [3] M. AsanoT. Iryo and M. Kuwahara, Microscopic pedestrian simulation model combined with a tactical model for route choice behaviour, Transportation Research Part C: Emerging Technologies, 18 (2010), 842-855.  doi: 10.1016/j.trc.2010.01.005.
    [4] S. Bandini, S. Manzoni and G. Vizzari, Agent based modeling and simulation: An informatics perspective, Journal of Artificial Societies and Social Simulation, 12 (2009).
    [5] N. Bellomo and A. Bellouquid, On the modeling of crowd dynamics: Looking at the beautiful shapes of swarms, Networks and Heterogeneous Media, 6 (2011), 383-399.  doi: 10.3934/nhm.2011.6.383.
    [6] N. BellomoA. Bellouquid and D. Knopoff, From the microscale to collective crowd dynamics, Society for Industrial and Applied Mathematics Multiscale Modeling and Simulation, 11 (2013), 943-963.  doi: 10.1137/130904569.
    [7] N. Bellomo, A. Bellouquid, L. Gibelli and N. Outada, A Quest Towards a Mathematical Theory of Living Systems, Modeling and Simulation in Science, Engineering and Technology, Birkhäuser/Springer, Cham, 2017. doi: 10.1007/978-3-319-57436-3.
    [8] N. Bellomo and C. Dogbe, On the modeling of traffic and crowds: A survey of models, speculations, and perspectives, Society for Industrial and Applied Mathematics Review, 53 (2011), 409-463.  doi: 10.1137/090746677.
    [9] N. Bellomo and L. Gibelli, Toward a mathematical theory of behavioral-social dynamics for pedestrian crowds, Mathematical Models and Methods in Applied Sciences, 25 (2015), 2417-2437.  doi: 10.1142/S0218202515400138.
    [10] N. Bellomo and L. Gibelli, Behavioral crowds: Modeling and Monte Carlo simulations toward validation, Computers and Fluids, 141 (2016), 13-21.  doi: 10.1016/j.compfluid.2016.04.022.
    [11] N. BellomoL. Gibelli and N. Outada, On the interplay between behavioral dynamics and social interactions in human crowds, Kinetic and Related Models, 12 (2019), 397-409.  doi: 10.3934/krm.2019017.
    [12] N. BellomoD. Knopoff and J. Soler, On the difficult interplay between life, "Complexity", and mathematical sciences, Mathematical Models and Methods in Applied Sciences, 23 (2013), 1861-1913.  doi: 10.1142/S021820251350053X.
    [13] N. Bellomo, B. Piccoli and A. Tosin, Modeling crowd dynamics from a complex system viewpoint, Mathematical Models and Methods in Applied Sciences, 22 (2012), 1230004, 29 pp. doi: 10.1142/S0218202512300049.
    [14] V. J. Blue and J. L. Adler, Cellular automata microsimulation of bidirectional pedestrian flows, Journal of Transportation Research Record, 1678 (1999), 135-141.  doi: 10.3141/1678-17.
    [15] V. J. Blue and J. L. Adler, Cellular automata microsimulation for modeling bi-directional pedestrian walkways, Transportation Research Part B: Methodological, 35 (2000), 293-312.  doi: 10.1016/S0191-2615(99)00052-1.
    [16] C. BursteddeK. KlauckA. Schadschneider and J. Zittartz, Simulation of pedestrian dynamics using a two-dimensional cellular automaton, Physica A: Statistical Mechanics and its Applications, 295 (2001), 507-525.  doi: 10.1016/S0378-4371(01)00141-8.
    [17] N. ChooramunP. J. Lawrence and E. R. Galea, An agent based evacuation model utilising hybrid space discretisation, Safety Science, 50 (2012), 1685-1694.  doi: 10.1016/j.ssci.2011.12.022.
    [18] M. ChraibiU. KemlohA. Schadschneider and A. Seyfried, Force-based models of pedestrian dynamics, Networks and Heterogeneous Media, 6 (2011), 425-442.  doi: 10.3934/nhm.2011.6.425.
    [19] M. Chraibi, A. Tordeux, A. Schadschneider and A. Seyfried, Modelling of pedestrian and evacuation dynamics, Encyclopedia of Complexity and Systems Science Series, (2019), 649–669.
    [20] J. C. DaiX. Li and L. Liu, Simulation of pedestrian counter flow through bottlenecks by using an agent-based model, Physica A: Statistical Mechanics and its Applications, 392 (2013), 2202-2211.  doi: 10.1016/j.physa.2013.01.012.
    [21] J. Dijkstra, J. Jessurun and H. Timmermans, A multi-agent cellular automata model of pedestrian movement, Pedestrian and Evacuation Dynamics, (2001), 173–181.
    [22] B. Einarsson, Accuracy and Reliability in Scientific Computing, Environments, and Tools, 18. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2005. doi: 10.1137/1.9780898718157.
    [23] R. Glowinski, Finite element Methods for incompressible viscous flow, Handbook of Numerical Analysis, Handb. Numer. Anal., IX, North-Holland, Amsterdam, 9 (2013), 3-1176. 
    [24] D. Helbing, A mathematical model for the behavior of pedestrians, Behavioral Science, 36 (1991), 298-310.  doi: 10.1002/bs.3830360405.
    [25] D. HelbingI. J. FarkasP. Molnar and T. Vicsek, Simulation of pedestrian crowds in normal and evacuation situations, Pedestrian and Evacuation Dynamics, 21 (2002), 21-58. 
    [26] D. Helbing and P. Molnar, Social force model for pedestrian dynamics, Physical Review E, 51 (1998), 4282-4286.  doi: 10.1103/PhysRevE.51.4282.
    [27] D. Helbing and T. Vicsek, Optimal self-organization, New Journal of Physics, 1 (1999). doi: 10.1088/1367-2630/1/1/313.
    [28] R. L. Hughes, A continuum theory for the flow of pedestrians, Transportation Research Part B: Methodological, 36 (2002), 507-535.  doi: 10.1016/S0191-2615(01)00015-7.
    [29] A. JohanssonD. Helbing and P. K. Shukla, Specification of the social force pedestrian model by evolutionary adjustment to video tracking data, Advances in Complex Systems, 10 (2007), 271-288.  doi: 10.1142/S0219525907001355.
    [30] R. J. LeVeque, Numerical Methods for Conservation Laws, Second edition, Lectures in Mathematics ETH Zürich. Birkhäuser Verlag, Basel, 1992. doi: 10.1007/978-3-0348-8629-1.
    [31] X. M. Li, X. D. Yan, X. G. Li and J. F. Wang, Using cellular automata to investigate pedestrian conflicts with vehicles in crosswalk at signalized intersection, Discrete Dynamics in Nature and Society, 2012 (2012), 287502, 16 pp. doi: 10.1155/2012/287502.
    [32] S. B. LiuS. M. LoJ. Ma and W. L. Wang, An agent-based microscopic pedestrian flow simulation model for pedestrian traffic problems, IEEE Transactions on Intelligent Transportation Systems, 15 (2014), 992-1001.  doi: 10.1109/TITS.2013.2292526.
    [33] J. MoussaïdD. HelbingS. GarnierA. JohanssonM. Combe and G. Theraulaz, Experimental study of the behavioural mechanisms underlying self-organization in human crowds, Proceedings of the Royal Society B: Biological Sciences, 276 (2009), 2755-2762. 
    [34] A. Schadschneider, W. Klingsch, H. Kluepfel, T. Kretz, C. Rogsch, A. Seyfried, Evacuation dynamics: Empirical results, modeling and applications, Extreme Environmental Events: Complexity in Forecasting and Early Warning, (2011), 517–550.
    [35] A. Schadschneider and A. Seyfried, Empirical results for pedestrian dynamics and their implications for modeling, Networks and Heterogeneous Media, 6 (2011), 545-560.  doi: 10.3934/nhm.2011.6.545.
    [36] A. SeyfriedO. PassonB. SteffenM. BoltesT. Rupprecht and W. Klingsch, New insights into pedestrian flow through bottlenecks, Transportation Science, 43 (2009), 267-406.  doi: 10.1287/trsc.1090.0263.
    [37] A. ShendeM. P. Singh and P. Kachroo, Optimization-Based feedback control for pedestrian evacuation from an exit corridor, IEEE Transactions on Intelligent Transportation Systems, 12 (2011), 1167-1176.  doi: 10.1109/TITS.2011.2146251.
    [38] B. Steffen and A. Seyfried, Methods for measuring pedestrian density, flow, speed and direction with minimal scatter, Physica A: Statistical Mechanics and its Applications, 389 (2009), 1902-1910.  doi: 10.1016/j.physa.2009.12.015.
    [39] A. Turner and A. Penn, Encoding natural movement as an agent-based system: An investigation into human pedestrian behaviour in the built environment, Environment and Planning B: Urban Analytics and City Science, 29 (2002), 473-490.  doi: 10.1068/b12850.
    [40] A. U. Kemloh Wagoum, A. Tordeux and W. Liao, Understanding human queuing behaviour at exits: An empirical study, Royal Society Open Science, 4 (2017), 160896, 13 pp. doi: 10.1098/rsos.160896.
    [41] J. A. Ward, A. J. Evans and N. S. Malleson, Dynamic calibration of agent-based models using data assimilation, Royal Society Open Science, 3 (2016), 150703, 17 pp. doi: 10.1098/rsos.150703.
    [42] J. Zhang, W. Klingsch, A. Schadschneider and A. Seyfried, Transitions in pedestrian fundamental diagrams of straight corridors and T-junctions, Journal of Statistical Mechanics: Theory and Experiment, 2011 (2011). doi: 10.1088/1742-5468/2011/06/P06004.
    [43] B. Zhou, X. Wang and X. Tang, Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-Agents, 2012 IEEE Conference on Computer Vision and Pattern Recognition, (2012), 2871–2878.
  • 加载中



Article Metrics

HTML views(500) PDF downloads(234) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint