Crowd simulation is a crucial tool to assess risks and engineer crowd safety at events and in built infrastructure. Simulations can be used for what-if studies, for real-time predictions, as well as to develop regulations for crowd safety. A reliable prediction requires a carefully calibrated model. Model parameters are often calibrated as point estimates, single parameter values for which the model evaluation fits given data best. In contrast, Bayesian inference provides a full posterior distribution for the fitted parameters that includes the residual uncertainty after calibration. In this work, we calibrate a microscopic model and an emulator derived from a microscopic model for crowd dynamics using point estimates and Approximate Bayesian Computation. We calibrate on data measuring the flow through a key scenario of crowd safety: a bottleneck. We vary the bottleneck width and demonstrate via three case studies the advantages and shortcomings of the two calibration techniques. In a case with a unimodal posterior, both methods yield similar results. However, one safety-relevant case study, that mimics the dynamics of evacuating people squeezing through an opening, exhibits a faster-is-slower dynamic where multiple free-flow speeds lead to the same flow. In this case, only Bayesian inference reveals the true bimodal shape of the posterior distribution. For multidimensional calibration, we illustrate that Bayesian inference allows accurate calibration by describing parameter relations. We conclude that, in practice, point estimation often seems sufficient, but Bayesian inference methods are necessary to capture important structural information about the uncertain parameters, and thus the physics of safety.

人群模拟是评估活动和建筑基础设施中的风险和设计人群安全的重要工具。模拟可用于假设研究、实时预测以及制定人群安全法规。可靠的预测需要仔细校准的模型。模型参数通常被校准为点估计，模型评估最适合给定数据的单个参数值。相比之下，贝叶斯推理为拟合参数提供了完整的后验分布，其中包括校准后的剩余不确定性。在这项工作中，我们使用点估计和近似贝叶斯计算校准了微观模型和从微观模型派生的模拟器，用于人群动态。我们校准通过人群安全的一个关键场景测量流量的数据：瓶颈。我们改变了瓶颈宽度，并通过三个案例研究展示了两种校准技术的优点和缺点。在具有单峰后验的情况下，两种方法产生相似的结果。然而，一个与安全相关的案例研究模拟了疏散人员通过开口的动力学，表现出一种越快越慢的动力学，其中多个自由流动速度导致相同的流动。在这种情况下，只有贝叶斯推理才能揭示后验分布的真实双峰形状。对于多维校准，我们通过描述参数关系说明贝叶斯推理允许准确校准。我们得出的结论是，在实践中，点估计通常似乎就足够了，但是贝叶斯推理方法对于捕获有关不确定参数的重要结构信息是必要的，