Agent-based modelling is a valuable approach for systems whose behaviour is driven by the interactions between distinct entities. They have shown particular promise as a means of modelling crowds of people in streets, public transport terminals, stadiums, etc. However, the methodology faces a fundamental difficulty: there are no established mechanisms for dynamically incorporating real-time data into models. This limits simulations that are inherently dynamic, such as pedestrian movements, to scenario testing of, for example, the potential impacts of new architectural configurations on movements. This paper begins to address this fundamental gap by demonstrating how a particle filter could be used to incorporate real data into an agent-based model of pedestrian movements at run time. The experiments show that it is indeed possible to use a particle filter to perform online (real time) model optimisation. However, as the number of agents increases, the number of individual particles (and hence the computational complexity) required increases exponentially. By laying the groundwork for the real-time simulation of crowd movements, this paper has implications for the management of complex environments (both nationally and internationally) such as transportation hubs, hospitals, shopping centres, etc.

中文翻译：

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使用基于代理的建模和粒子过滤器实时模拟人群

对于行为受不同实体之间的交互作用驱动的系统，基于代理的建模是一种有价值的方法。他们已经显示出特别的希望，可以作为在街道，公共交通枢纽，体育场等人群中进行建模的一种方法。但是，该方法面临着一个根本性的困难：没有建立将动态数据实时整合到模型中的机制。这将固有动态的模拟（例如行人运动）限制在方案测试中，例如，场景测试新建筑配置对运动的潜在影响。本文通过演示如何使用粒子过滤器将真实数据合并到运行时基于代理的行人运动模型中，来解决这一基本差距。实验表明，确实有可能使用粒子过滤器进行在线（实时）模型优化。但是，随着代理数量的增加，所需的单个粒子的数量（以及因此的计算复杂度）将呈指数增长。通过为实时模拟人群运动打下基础，本文对交通枢纽，医院，购物中心等复杂环境（国内外）的管理产生了影响。