Placing large group relations into pedestrian dynamics: psychological crowds in counterflow

Understanding influences on pedestrian movement is important to accurately simulate crowd behaviour, yet little research has explored the psychological factors that influence interactions between large groups in counterflow scenarios. Research from social psychology has demonstrated that social identities can influence the micro-level pedestrian movement of a psychological crowd, yet this has not been extended to explore behaviour when two large psychological groups are co-present. This study investigates how the presence of large groups with different social identities can affect pedestrian behaviour when walking in counterflow. Participants (N = 54) were divided into two groups and primed to have identities as either ‘team A’ or ‘team B’. The trajectories of all participants were tracked to compare the movement of team A when walking alone to when walking in counterflow with team B, based on their i) speed of movement and distance walked, and ii) proximity between participants. In comparison to walking alone, the presence of another group influenced team A to collectively self-organise to reduce their speed and distance walked in order to walk closely together with ingroup members. We discuss the importance of incorporating social identities into pedestrian group dynamics for empirically validated simulations of counterflow scenarios

Social identity processes associated with perceived risk at pilot sporting events during COVID‐19

Previous research suggests that shared social identification and expected support from others can reduce the extent to which attendees of mass events perceive that others pose health risks. This study evaluated the social identity processes associated with perceived risk at UK pilot sporting events held during COVID‐19, including the government Events Research Programme. An online survey (N = 2029) measured attendee perceptions that other spectators adhered to safety measures, shared social identity with other attendees, expectations that others would provide support, and the perceived risk of germ spread from other attendees. Results indicate that for football attendees, seeing others adhering to COVID‐19 safety measures was associated with lower perceived risk and this was partially mediated via increased shared social identity and expected support. However, the sequential mediations were non‐significant for rugby and horse racing events. The decreased perceived risk for football and rugby attendees highlights the importance of understanding social identity processes at mass events to increase safety. The non‐significant associations between shared social identity and perceived risk and between expected support and perceived risk for both the rugby and the horse racing highlights the need to further research risk perceptions across a range of mass event contexts

Group processes in emergency evacuation

Dr Anne Templeton and Dr Yunhe Tong discuss recent research into the importance of group processes in emergency evacuations. When considering how people will react in evacuations, planners presume which information people will attend to, whose instructions they will follow, which evacuation routes they will take, and how long they will spend before initiating evacuation. To understand these reactions, much research has focused on important issues affecting evacuation behaviour such as how clear the evacuation guidance is, how much knowledge the evacuees have about the environment, and how relevant evacuees believe a threat is to them personally

A dynamic state-based model of crowds

We consider the problem of categorizing and describing the dynamic properties and behaviours of crowds over time. Previous work has tended to focus on a relatively static "typology"-based approach, which does not account for the fact that crowds can change, often quite rapidly. Moreover, the labels attached to crowd behaviours are often subjective and/or value-laden. Here, we present an alternative approach, loosely based on the statechart formalism from computer science. This uses relatively "agnostic" labels, which means that we do not prescribe the behaviour of an individual, but provide a context within which an individual might behave. This naturally describes the time-series evolution of a crowd as "threads" of states, and allows for the dynamic handling of an arbitrary number of "sub-crowds".Comment: Presented at the 2023 Pedestrian and Evacuation Dynamics Conference, Eindhoven, The Netherlands, June 28-30 202

Physical crowds and psychological crowds: applying self-categorization theory to computer simulation of collective behaviour

Computer models are used to simulate pedestrian behaviour for safety at mass events. Previous research has indicated differences between physical crowds of co-present individuals, and psychological crowds who mobilise collective behaviour through a shared social identity. This thesis aimed to examine the assumptions models use about crowds, conduct two studies of crowd movement to ascertain the behavioural signatures of psychological crowds, and implement these into a theoretically-driven model of crowd behaviour. A systematic review of crowd modelling literature is presented which explores the assumptions about crowd behaviour being used in current models. This review demonstrates that models portray the crowd as either an identical mass with no inter-personal connections, unique individuals with no connections to others, or as small groups within a crowd. Thus, no models have incorporated the role of self-categorisation theory needed to simulate collective behaviour. The empirical research in this thesis aimed to determine the behavioural effects of self-categorisation on pedestrian movement. Findings from a first study illustrate that, in comparison to a physical crowd, perception of shared social identities in the psychological crowd motivated participants to maintain close proximity with ingroup members through regulation of their speed and distance walked. A second study showed that collective self-organisation seemed to be increased by the presence of an outgroup, causing ingroup members to tighten formation to avoid splitting up. Finally, a computer model is presented which implements the quantified behavioural effects of self-categorisation found in the behavioural studies. A self-categorisation parameter is introduced to simulate ingroup members self-organising to remain together. This is compared to a physical crowd simulation with group identities absent. The results demonstrate that the self-categorisation parameter provides more accurate simulation of psychological crowd behaviour. Thus, it is argued that models should implement self-categorisation into simulations of psychological crowds to increase safety at mass events