Balancing between simulation performance and simulation accuracy is difficult. Most simulators focus on optimizing only one of these two aspects. Which comes at the cost of the other. In this work we demonstrate a framework to dynamically balance between computational performance and simulation accuracy based on the context of the simulation. This allows to maximize both performance and accuracy when possible during the execution of the simulation. To do this, we present a method to apply adaptive abstraction in a large-scale agent-based traffic simulation. This method uses the concept of experimental frames for simulation models in order to keep track of model validity given the current simulation context. Furthermore, we present a custom developed state-of-the-art multi-level traffic simulator that includes adaptive abstraction in the core of its simulation architecture. We validate the proposed methods in a realistic multi-level traffic simulation use case executed in an urban environment.

在仿真性能和仿真精度之间取得平衡是很困难的。大多数模拟器只关注优化这两个方面之一。这是以另一个为代价的。在这项工作中，我们展示了一个框架，可以根据模拟的上下文在计算性能和模拟精度之间动态平衡。这允许在模拟执行期间尽可能最大化性能和准确性。为此，我们提出了一种在大规模基于代理的交通模拟中应用自适应抽象的方法。该方法将实验框架的概念用于仿真模型，以便在给定当前仿真上下文的情况下跟踪模型有效性。此外，我们展示了一个定制开发的最先进的多级交通模拟器，在其模拟架构的核心中包含自适应抽象。我们在城市环境中执行的现实多级交通模拟用例中验证了所提出的方法。