Automated Calibration of a Microscopic Traffic Flow Simulation Using Real-World Observations

In order to evaluate future mobility services as well as automated driving functions, a virtual environment is required. As a key component of this virtual proving ground, a traffic flow simulation is necessary to represent real-world traffic conditions. Real-world observations, such as historical traffic counts and traffic light state information, provide a basis for the representation of these conditions in the simulation. A main challenge arises in allocating these available real-world data in the virtual world for calibration purposes. In this work, we therefore propose a scalable approach to transfer real-world data, exemplarily taken from the German city Ingolstadt, to a virtual environment for a calibration of a traffic flow simulation. For the allocation of historical real-world data, we use a process which matches real-world measurements with their corresponding locations in the virtual environment. The calibration incorporates the replication of realistic traffic light programs as well as the adjustment of simulated traffic flows. The proposed calibration procedure allows for an automated creation of a calibrated traffic flow simulation of an arbitrary road network given historical real-world observations.