RightsACM allow an authors' version of their own ACMcopyrighted work on their personal server or on servers belonging to their employers. As a collective and highly dynamic social group, human crowd is a fascinating phenomenon which has been constantly concerned by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This paper assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the timescale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user. We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in these work. We have also discussed some open problems in the area. This paper will provide the researchers with useful information and insights on the state-of-the-art of the technologies in crowd modeling and simulation as well as future research directions.

Human crowd is a fascinating social phenomenon in nature. This paper presents our work on designing behavior model for virtual humans in a crowd simulation under normal-life and emergency situations. Our model adopts an agent-based approach and employs a layered framework to reflect the natural pattern of human-like decision making process, which generally involves a person's awareness of the situation and consequent changes on the internal attributes. The social group and crowd-related behaviors are modeled according to the findings and theories observed from social psychology (e.g., social attachment theory). By integrating our model into an agent execution process, each individual agent can response differently to the perceived environment and make realistic behavioral decisions based on various physiological, emotional, and social group attributes. To demonstrate the effectiveness of our model, a case study has been conducted, which shows that realistic human behaviors can be generated at both individual and group level.

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Previous studies have suggested that virtual reality (VR) can elicit emotions in different visual modes using 2D or 3D headsets. However, the effects on emotional arousal by using these two visual modes have not been comprehensively investigated, and the underlying neural mechanisms are not yet clear. This paper presents a cognitive psychological experiment that was conducted to analyze how these two visual modes impact emotional arousal. Forty volunteers were recruited and were randomly assigned to two groups. They were asked to watch a series of positive, neutral and negative short VR videos in 2D and 3D. Multichannel electroencephalograms (EEG) and skin conductance responses (SCR) were recorded simultaneously during their participation. The results indicated that emotional stimulation was more intense in the 3D environment due to the improved perception of the environment; greater emotional arousal was generated; and higher beta (21–30 Hz) EEG power was identified in 3D than in 2D. We also found that both hemispheres were involved in stereo vision processing and that brain lateralization existed in the processing.