There has been a significant increase in the modelling, analysis and calibration of models for pedestrian crowds in the last years. In this talk I will present different mathematical models for crowds – such as the social force model or the Hughes model – and discuss their respective analytical and computational challenges. I will then focus on the problem of estimating parameters in macroscopic pedestrian models using trajectory data. I will use the Bayesian framework to perform the identification and analyse the performance of the developed methodologies for different experimental settings.
This video was produced by the SITE Research Center at New York University, as part of their talk series.
