Application of Ensemble Kalman Filter to Pedestrian Flow

Fumiya Togashi, Takashi Misaka, Rainald Löhner, Shigeru Obayashi


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.


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

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Copyright (c) 2020 Fumiya Togashi, Takashi Misaka, Rainald Löhner, Shigeru Obayashi

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