ABSTRACT
Crowds of pedestrians are complex entities from different points of view, starting from the difficulty in providing a satisfactory definition of the term “crowd”. “(Too) many people in (too) little space” [Kruse, 1986] is a pedestrian crowd definition aggregating several disciplinary interpretations, and the range of this definition sites its fuzzy borders in the traditional opposition between humanistic and scientific cultures in these studies. The range of individual and collective behaviors that take place in a crowd, the composite mix of competition for the shared space but also collaboration due to, not necessarily explicit but shared, social norms, the possibility to detect self-organization and emergent phenomena; they are all indicators of the intrinsic complexity of a crowd. Nonetheless, the relevance of human behavior, and especially of the movements of pedestrians in a built environment in normal and extraordinary situations (e.g., evacuation), and its implications for the activities of architects, designers and urban planners are apparent (see, e.g., [Batty, 2001] and [Willis et al., 2004]), especially given recent dramatic episodes such as terrorist attacks, riots and
fires, but also due to the growing issues in facing the organization and management of public events (ceremonies, races, carnivals, concerts, parties/social gatherings, and so on) and in designing naturally crowded places (e.g., stations, arenas, airports). Crowd models and simulators are thus increasingly being investigated in the scientific context, sold by firms∗, and used by decision makers. In fact, even if research on this topic is still quite lively and far from a complete understanding of the complex phenomena related to crowds of pedestrians in the environment, models and simulators have shown their usefulness in supporting architectural designers and urban planners in their decisions by creating the possibility to envision the behavior/movement of crowds of pedestrians in specific designs/environments, to elaborate what-if scenarios and evaluate their decisions with reference to specific metrics and criteria.
