Application of Ensemble Kalman Filter to Pedestrian Flow


  • Fumiya Togashi Applied Simulations Inc., Potomac, USA
  • Takashi Misaka Frontier Research Institute for Interdisciplinary Science, Tohoku University, Sendai, Japan
  • Rainald Löhner George Mason University, Fairfax, USA
  • Shigeru Obayashi Institute of Fluid Science, Tohoku University, Sendai, Japan



computational crowd dynamics, ensemble kalman filter, data assimilation, pedestrian flow


We adopted the Ensemble Kalman Filter (EnKF) methodology in our computational simulation code for pedestrian flows. The EnKF, which is a type of data assimilation methodology, has been developed in the field of weather forecast where the atmospheric condition varies hour by hour. The EnKF estimates the parameters or boundary/initial conditions in the numerical model based on the updated measured data. We considered the EnKF a promising tool for the simulation of pedestrian flows, which are notoriously difficult to predict. In this study, two scenarios were conducted to confirm the usefulness of the EnKF. The first case was unidirectional pedestrian flow in straight corridors, and the second case was Mataf scenario at the Kaaba in Mecca. Needless to say, the second scenario was very challenging because of the number of pilgrims and the degrees of freedom. In each scenario, we conducted the numerical simulation using the original parameter set and then applied the EnKF to improve the accuracy of the simulation.


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How to Cite

Togashi, F., Misaka, T., Löhner, R., & Obayashi, S. (2020). Application of Ensemble Kalman Filter to Pedestrian Flow. Collective Dynamics, 5, 467–470.



Proceedings of Pedestrian and Evacuation Dynamics 2018