For example, crowd simulation techniques have been actively employed to assess evacuation safety for various hazardous events, such as fires 2, floods 3 and tsunamis 4. Over the past few decades, various crowd simulation models have been developed 1, and the utilisation of the crowd simulation has rapidly increased in our society. These results support the feasibility of real-time crowd flow forecasting and subsequent crowd management, even for large but microscopic crowd problems. Numerical experiments, including a realistic evacuation scenario with 5000 individuals, demonstrated that the present method could successfully provide reasonable crowd flow forecasting for different crowd scenarios, even with limited information on crowd movements. By sequentially estimating both the crowd state and the latent parameter behind the crowd flows from the aggregate crowd density observation with the particle filter algorithm, the present method estimates and forecasts the large crowd flow using agent-based simulations that incorporate observation data. Here, we present a method that incorporates crowd observation data to forecast a large crowd flow, including thousands of individuals, using a microscopic agent-based model. Recent studies have attempted to incorporate the real-time crowd observations into crowd simulations for real-time crowd forecasting and management however, crowd flow forecasting considering individual-level microscopic interactions, especially for large crowds, is still challenging. Unlike conventional crowd simulations for what-if analysis, agent-based crowd simulations for real-time applications are an emerging research topic and an important tool for better crowd managements in smart cities.
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