Automated Calibration of Traffic Demand and Traffic Lights in SUMO Using Real-World Observations

Abstract

Virtual traffic environments allow for evaluations of automated driving functions as well as future mobility services. 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. 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 in SUMO. To recreate measured traffic properties such as traffic counts or traffic light programs into the simulation, the measurement sites must first be allocated in the virtual environment. For the allocation of historical real-world data, a matching procedure is applied, in order to associate 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.