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In this paper we propose a classification of crowd models in builtenvironments based on the assumed pedestrian ability to foresee the movementsof other walkers. At the same time, we introduce a new family of macroscopicmodels, which make it possible to tune the degree of predictiveness (i.e.,rationality) of the individuals. By means of these models we describe both thenatural behavior of pedestrians, i.e., their expected behavior according totheir real limited predictive ability, and a target behavior, i.e., aparticularly efficient behavior one would like them to assume (for, e.g.,logistic or safety reasons). Then we tackle a challenging shape optimizationproblem, which consists in controlling the environment in such a way that thenatural behavior is as close as possible to the target one, thereby inducingpedestrians to behave more rationally than what they would naturally do. Wepresent numerical tests which elucidate the role of rational/predictiveabilities and show some promising results about the shape optimization problem.

Modeling rationality to control self-organization of crowds: An environmental approach