  Social scientists have criticised computer models of pedestrian streams for
their treatment of psychological crowds as mere aggregations of individuals.
Indeed most models for evacuation dynamics use analogies from physics where
pedestrians are considered as particles. Although this ensures that the results
of the simulation match important physical phenomena, such as the deceleration
of the crowd with increasing density, social phenomena such as group processes
are ignored. In particular, people in a crowd have social identities and share
those social identities with the others in the crowd. The process of self
categorisation determines norms within the crowd and influences how people will
behave in evacuation situations. We formulate the application of social
identity in pedestrian simulation algorithmically. The goal is to examine
whether it is possible to carry over the psychological model to computer models
of pedestrian motion so that simulation results correspond to observations from
crowd psychology. That is, we quantify and formalise empirical research on and
verbal descriptions of the effect of group identity on behaviour. We use
uncertainty quantification to analyse the model's behaviour when we vary
crucial model parameters. In this first approach we restrict ourselves to a
specific scenario that was thoroughly investigated by crowd psychologists and
where some quantitative data is available: the bombing and subsequent
evacuation of a London underground tube carriage on July 7th 2005.
