基于Agent建模和粒子滤波的实时的人群模拟
Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter
摘要：
基于代理的建模是一种有价值的方法，其行为是由不同实体之间的相互作用驱动的系统。他们表现出特别的承诺，作为一种手段，在街道上，公共交通终端，体育场等人群的建模，然而，该方法面临着一个根本的困难：没有建立机制，动态地将实时数据模型。这限制了固有动态的模拟，例如行人运动，以场景测试，例如，新的建筑配置对运动的潜在影响。本文开始解决这个根本的差距，演示如何粒子滤波器可以用来将真实的数据到一个基于代理的行人运动模型在运行时间。实验结果表明，粒子滤波器用于在线（实时的）模型优化是可行的。然而，随着代理数量的增加，所需的单个粒子的数量（以及因此所需的计算复杂性）呈指数级增加。通过为人群流动的实时模拟奠定基础，本文对复杂环境（国内和国际）的管理有影响，如交通枢纽，医院，购物中心等。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
