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Top1. Introduction
Grouping is a common phenomenon in pedestrian crowds. According to the work of (Adrian, 1997; Loscos, Marchal, & Meyer, 2003), pedestrian crowds contain both grouped and isolated individuals. In the settings of a city, less than a half of the pedestrians walk alone (Loscos et al., 2003). For example, in a shopping mall, family members stay beside each other and maintain the group in a clustered way during the activity (Qiu & Hu, 2009). Grouped pedestrians can be found in emergency situations as well. According to social proof theory, when an individual lacks objective evaluation in the emergency (e.g., evacuate from a building in fire), the individual tends to follow the actions of others as a guide on how he/she might act. One example of social proof is the herding behavior – when under highly emergent situations, an individual tends to follow others almost blindly (Pan, Han, Dauber, & Law, 2007). This is an example where people form social groups dynamically and follow the “leaders” spontaneously.
Social groups play important roles in affecting crowd behaviors. Social groups affect the flow of pedestrian crowds as well as the evacuation efficiency in emergency situations. As discussed in Santos and Aguirre (2004), a leader-follower group may be more smooth and efficient than a clustered group if the group has a large number of members. In this case, a clustered group can result in slow movement, especially in a constrained area. The work of Yang, Zhao, Li, and Fang (2005) simulates the kin behavior in emergent evacuations and shows that the number of sub-groups and the members in each sub-group influence the evacuation efficiency significantly. The work of Klupfel, Meyer-Konig, and Schreckenberg (2004) studies the effect of group size on the walking speed through controlled experiments and concludes that the walking speed decreases as group size increases.
Social group has been an active research topic in sociology and psychology where a group is generally defined as a set of collected persons who share common goals and norms (McDougall, 1920). Although social groups are extensively studied in socio-psychology, how to model social groups is still an open issue (Klupfel, et al., 2004) and group modeling is not incorporated into most pedestrian crowd models. Group modeling is a challenging task because of the non-linear interactions in pedestrian crowds and the dissimilarity nature of pedestrians. Many factors need to be considered, such as individual characteristics, group size, relationships among groups, and influences among group members (Braun, Musse, de Oliveira, & Bodmann, 2003; Santos & Aguirre, 2004). Existing work for simulating social groups mainly focuses on the perspective of reproducing some group-level behaviors based on specific social or psychological theories, such as social comparison theory (Festinger, 1954; Fridman & Kaminka, 2007) or five-factor personality model (Durupinar, Allbeck, Pelechano, & Badler, 2008; Ghasem-Aghaee & Oren, 2007; Jaganathan, Clarke, Koshti, Kaup, & Oleson, 2007). Even though these existing works study some aspects of grouping, they do not provide a unified framework to explicitly model the intra-relationship of group members and inter-connections between groups for exploring the effect of different social or psychological factors on the grouping behavior.