Optimization-based Calibration for Micro-level Agent-based Simulation of Pedestrian Behavior in Public Spaces

Pedestrian and crowd models have been on a rise over the past few decades which has been marked by emergence of a multitude of approaches and their implementations. However, determining and proving whether particular models reproduce the intended process adequately is still a complex task. Not only do researchers aim to assure that proposed algorithms correlate with the real-world mechanisms of collective behavior, but seek ways to adjust the crucial parameters of the model that vary between the cases to the observations and experimental results. Following this line of thought, we have calibrated our pedestrian simulator through rather frugal optimization of parameters with genetic algorithms to the data derived from the analysis of the real-world reference data. We present the results of such optimization along with the description of the case studied and the overall process of data collection. The paper is concluded with a discussion of the results of the calibration, observations made throughout the research and perspectives for further studies.