Adaptivity in multi-level traffic simulation using experimental frames

Abstract

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.