Parameter Calibration in Crowd Simulation Models using Approximate Bayesian Computation

Nikolai Bode


Simulation models for pedestrian crowds are a ubiquitous tool in research and industry. It is crucial that the parameters of these models are calibrated carefully and ultimately it will be of interest to compare competing models to decide which model is best suited for a particular purpose. In this contribution, I demonstrate how Approximate Bayesian Computation (ABC), which is already a popular tool in other areas of science, can be used for model fitting and model selection in a pedestrian dynamics context. I fit two different models for pedestrian dynamics to data on a crowd passing in one direction through a bottleneck. One model describes movement in continuous-space, the other model is a cellular automaton and thus describes movement in discrete-space. In addition, I compare models to data using two metrics. The first is based on egress times and the second on the velocity of pedestrians in front of the bottleneck. My results show that while model fitting is successful, a substantial degree of uncertainty about the value of some model parameters remains after model fitting. Importantly, the choice of metric in model fitting can influence parameter estimates. Model selection is inconclusive for the egress time metric but supports the continuous-space model for the velocity-based metric. These findings show that ABC is a flexible approach and highlights the difficulties associated with model fitting and model selection for pedestrian dynamics. ABC requires many simulation runs and choosing appropriate metrics for comparing data to simulations requires careful attention. Despite this, I suggest ABC is a promising tool, because it is versatile and easily implemented for the growing number of openly available crowd simulators and data sets.


pedestrian dynamics; simulation model; parameter calibration; statistical analysis; approximate bayesian computation

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D.C. Duives, W. Daamen, and S.P. Hoogendoorn. "State-of-the-art crowd motion simulation models." Transp. Res. Part C, vol. 37, pp. 193-209, 2013.

W. Liao, J. Zhang, X. Zheng, and Y. Zhao. "A generalized validation procedure for pedestrian models." Simul. Model. Pract. Theor., vol. 77, pp. 20-31, 2017.

M. Chraibi, T. Ensslen, H. Gottschalk, M. Saadi, and A. Seyfried. "Assessment of models for pedestrian dynamics with functional principal component analysis." Physica A, vol. 451, pp. 475-489, 2016.

D. Wolinski, S.J Guy, A‐H. Olivier, M. Lin, D. Manocha, and J. Pettré. "Parameter estimation and comparative evaluation of crowd simulations." Comput. Graph. Forum, vol. 33, no. 2, pp. 303-312. 2014.

S. Seer, C. Rudloff, T. Matyus, and N. Brändle. "Validating social force based models with comprehensive real world motion data." Transp. Res. Proc., vol. 2, pp. 724-732, 2014.

R. Lovreglio, E. Ronchi, and D. Nilsson. "Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization." Physica A, vol. 438, pp. 308-320. 2015.

M. Li, Y. Zhao, L. He, W. Chen, and X. Xu. "The parameter calibration and optimization of social force model for the real-life 2013 Ya’an earthquake evacuation in China." Safety Sci., vol. 79, pp. 243-253, 2015.

W. Zeng, P. Chen, G. Yu, and Y. Wang. "Specification and calibration of a microscopic model for pedestrian dynamic simulation at signalized intersections: A hybrid approach." Transp. Res. Part C, vol. 80, pp. 37-70, 2017.

T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M.P.H. Stumpf. "Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems." J. R. Soc. Interface, vol. 6, no. 31, pp. 187-202, 2009.

D. Helbing, I. Farkas, and T. Vicsek. "Simulating dynamical features of escape panic." Nature, vol. 407, no. 6803, pp. 487, 2000.

N.W.F. Bode, and E.A. Codling. "Human exit route choice in virtual crowd evacuations." Anim. Behav., vol. 86, no. 2, pp. 347-358, 2013.

C. Burstedde, K. Klauck, A. Schadschneider, and J. Zittartz. "Simulation of pedestrian dynamics using a two-dimensional cellular automaton." Physica A, vol. 295, no. 3, pp. 507-525, 2001.

J. Liddle, A. Seyfried, W. Klingsch, T. Rupprecht, A. Schadschneider, and A. Winkens. "An experimental study of pedestrian congestions: influence of bottleneck width and length" in Proceedings of International Conference on Traffic and Granular Flow 2009, Shanghai, China, 2009.

A. Garcimartín, J.M. Pastor, C. Martín-Gómez, D. Parisi, and I. Zuriguel. "Pedestrian collective motion in competitive room evacuation". Sci. Rep., vol. 7, no. 1, pp. 10792, 2017.

I. von Sivers, and G. Köster. "Dynamic stride length adaptation according to utility and personal space". Transp. Res. Part B, vol. 74, pp.104-117, 2015.

R.E. Kass, and A.E. Raftery. "Bayes factors". J. Am. Stat. Assoc., vol. 90, no. 430, pp.773-795, 1995.

F. Albrecht, B. Degenhart, F. Dietrich, M. Gödel, B. Kleinmeier, G. Köster, M. Laubinger, D. Lehmberg, J. Schöttl, S. Schuhbäck, M. Seitz, S. Stemmer, I. von Sivers, M.T. Parente, and B. Zönnchen. (2018, May 31). Vadere [Online]. Available:

E. Andresen, M. Chraibi, A. Graf, D. Haensel, W. Liao, U. Kemloh, M. Osterkamp, A. Portz, O. Schmidts, B. Schröder, D. Shhikhalev, A. Tordeux, J. Zhang, A. Schumacher, N. Sohre, and Y. Xiao. (2018, May 31). JuPedSim [Online]. Available:

S. Curtis, A. Best, and D. Manocha. (2018, May 31). Menge [Online]. Available:


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