基于近似贝叶斯计算的人群模拟模型参数校正
Parameter Calibration in Crowd Simulation Models using Approximate Bayesian Computation
摘要：
行人人群的仿真模型是研究和工业中无处不在的工具。这些模型的参数必须经过仔细校准，最后比较相互竞争的模型，以决定哪种模型最适合某一特定目的，这将是很有意义的。在这方面的贡献，我演示了近似贝叶斯计算（ABC），这已经是一个流行的工具，在其他领域的科学，可以用于模型拟合和模型选择在行人动态的背景下。我将两种不同的行人动力学模型与人群通过瓶颈的数据进行了拟合。一个模型描述连续空间中的运动，另一个模型是元胞自动机，因此描述离散空间中的运动。此外，我使用两个指标来比较模型和数据。第一个是基于出口时间，第二个是基于行人在瓶颈前面的速度。我的研究结果表明，虽然模型拟合是成功的，相当程度的不确定性的一些模型参数的值仍然模型拟合后。重要的是，模型拟合中度量的选择会影响参数估计。模型选择对于出口时间度量是不确定的，但是对于基于速度的度量支持连续空间模型。这些研究结果表明，ABC是一种灵活的方法，并强调与模型拟合和模型选择的行人动力学的困难。ABC需要多次模拟运行，选择适当的指标来比较数据和模拟需要仔细注意。尽管如此，我认为ABC是一个很有前途的工具，因为它是通用的，很容易实现的，越来越多的公开可用的人群模拟器和数据集。
英文摘要：
Simulation models for pedestrian crowds are a ubiquitous tool in research and industry. It is crucial that the parameters of these models are calibrated carefully and ultimately it will be of interest to compare competing models to decide which model is best suited for a particular purpose. In this contribution, I demonstrate how Approximate Bayesian Computation (ABC), which is already a popular tool in other areas of science, can be used for model fitting and model selection in a pedestrian dynamics context. I fit two different models for pedestrian dynamics to data on a crowd passing in one direction through a bottleneck. One model describes movement in continuous-space, the other model is a cellular automaton and thus describes movement in discrete-space. In addition, I compare models to data using two metrics. The first is based on egress times and the second on the velocity of pedestrians in front of the bottleneck. My results show that while model fitting is successful, a substantial degree of uncertainty about the value of some model parameters remains after model fitting. Importantly, the choice of metric in model fitting can influence parameter estimates. Model selection is inconclusive for the egress time metric but supports the continuous-space model for the velocity-based metric. These findings show that ABC is a flexible approach and highlight the difficulties associated with model fitting and model selection for pedestrian dynamics. ABC requires many simulation runs and choosing appropriate metrics for comparing data to simulations requires careful attention. Despite this, I suggest ABC is a promising tool, because it is versatile and easily implemented for the growing number of openly available crowd simulators and data sets.
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。
