Developing an understanding of human crowd movement is critical for optimizing crowd safety, both on the streets and in buildings. It is well understood from the study of animal collective motion that individuals are better able coordinate and respond to perturbations by perceiving movements from their neighbors (Ballerini et al., 2008; Couzin & Krause, 2003; Partridge, 1982). Thus, modeling and simulating human crowd behavior depends on the modeler’s ability to accurately characterize how an individual is influenced by his or her neighbors (Ballerini et al., 2008); i.e., identifying the local rules of engagement that lead to self-organization (Rio et al., 2018) – a process in which coordination emerges solely from the interactions of these local rules (Couzin & Krause, 2003). Popular models that are used today for human crowd simulation are theory-driven (Moussaïd et al., 2009, 2011; Sumpter et al., 2012), based on particle behavior (Helbing & Molnar, 1995) as opposed to data-driven, based on human behavior (Rio et al., 2018). This creates a serious problem: These models generate local trajectories that do not realistically simulate pedestrian behavior, resulting in many collisions between individuals (Pelechano et al., 2007). We at the VENLab employ an alternative approach: develop a data-driven crowd model by studying real human behavior. Previous VENLab experiments have demonstrated that when walking with a crowd, pedestrians form their alignment decision by averaging over the local neighborhood within a soft-metric radius of about 5m (Rio et al., 2018; Wirth, Dachner, Rio, & Warren, in Review; Willcoxon & Warren, in Preparation). In further exploring the rules of engagement that lead to self-organized crowd behavior, there are two critical questions that my dissertation work addresses: 1. How is an individual pedestrian recruited into collective motion? And 2. When a pedestrian is following a crowd that splits in disparate directions, what rules dictate which group the pedestrian follows? While the dissertation consists of five empirical studies – with model modifications that were inspired by the experimental insights – I will report the two most important findings here that answer the questions posed above. An unfinished study, which is currently being completed, is discussed in Future Directions.
Wirth, Trenton D., "Modeling Self-Organization in Human Crowds" (2020). Link Foundation Modeling, Simulation and Training Fellowship Reports. 41.
Link Foundation Fellowship for the years 2019-2020.