Using ensemble Kalman filter to determine parameters for computational crowd dynamics simulations. Issue 7 (22nd October 2018)

• Record Type: Journal Article
• Authors: Togashi, Fumiya; Misaka, Takashi; Löhner, Rainald; Obayashi, Shigeru
• Is Part Of: Engineering computations. Volume 35:Issue 7(2018)
• Page Start: 2612
• Page End: 2628
• Publication Date: 2018-10-22
• Subjects: Computational crowd dynamics -- Data assimilation -- Ensemble Kalman filter -- Pedestrian flow; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-03-2018-0115
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: It is of paramount importance to ensure safe and fast evacuation routes in cities in case of natural disasters, environmental accidents or acts of terrorism. The same applies to large-scale events such as concerts, sport events and religious pilgrimages as airports and to traffic hubs such as airports and train stations. The prediction of pedestrian is notoriously difficult because it varies depending on circumstances (age group, cultural characteristics, etc.). In this study, the Ensemble Kalman Filter (EnKF) data assimilation technique, which uses the updated observation data to improve the accuracy of the simulation, was applied to improve the accuracy of numerical simulations of pedestrian flow. Design/methodology/approach: The EnKF, one of the data assimilation techniques, was applied to the in-house numerical simulation code for pedestrian flow. Two cases were studied in this study. One was the simplified one-directional experimental pedestrian flow. The other was the real pedestrian flow at the Kaaba in Mecca. First, numerical simulations were conducted using the empirical input parameter sets. Then, using the observation data, the EnKF estimated the appropriate input parameter sets. Finally, the numerical simulations using the estimated parameter sets were conducted. Findings: The EnKF worked on the numerical simulations of pedestrian flow very effectively. In both cases: simplified experiment and real pedestrian flow, the EnKF estimated the proper input parameter sets which greatly improved the accuracy of the numerical simulation. The authors believe that the technique such as EnKF could also be used effectively in other fields of computational engineering where simulations and data have to be merged. Practical implications: This technique can be used to improve both design and operational implementations of pedestrian and crowd dynamics predictions. It should be of high interest to command and control centers for large crowd events such as concerts, airports, train stations and pilgrimage centers. Originality/value: To the authors' knowledge, the data assimilation technique has not been applied to a numerical simulation of pedestrian flow, especially to the real pedestrian flow handling millions pedestrian such as the Mataf at the Kaaba. This study validated the capability and the usefulness of the data assimilation technique to numerical simulations for pedestrian flow.