In recent years, digital twin technologies, which reproduce various data from the real world in virtual space, have been attracting attention due to a rapid increase in computational resources. In the field of crowd control, multiagent simulation (MAS) enables precise simulation of crowd movements and is anticipated to be used for evaluating how to guide crowds, such as evacuation efficiency and potential risks of crowd accidents. In this context, the fidelity of the crowd simulation is a crucial factor. In the Evolutionary Computing Competition 2022, an optimization competition was held to estimate pedestrian traffic information from observed data using MAS. This paper outlines the formulation of the optimization problem and discusses the problem’s characteristics by introducing the optimization algorithms and their results provided by the participating teams.
