Coupling an Agent-Based Model and Ensemble Kalman Filter for Real-Time Crowd Modelling

Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the Ensemble Kalman Filter (EnKF) with an agent-based crowd model to enhance its accuracy in real-time. Using the example of Grand Central Station in New York, we demonstrate how our approach can update the state of an agent-based model in real-time, aligning it with the evolution of the actual system. The findings reveal that the EnKF can substantially improve the accuracy of agent-based pedestrian simulations by assimilating data as they evolve. This approach not only offers efficiency advantages over existing methods but also presents a more realistic representation of a complex environment than most previous attempts. The potential applications of this method span the management of public spaces under ‘normality’ to exceptional circumstances such as disaster response, marking a significant advancement for real-time agent-based modelling applications