Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments

Abstract

Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics.

Geospatial Information Visualization and Extended Reality Displays

“…A modern description of a VGE is a digital geographic environment "generated by computers and related technologies that users can use to experience and recognize complex geographic systems and further conduct comprehensive geographic analyses, through equipped functions, including multichannel human-computer interactions (HCIs), distributed geographic modeling and simulations, and network geo-collaborations" (Chen and Lin 2018, p. 329). Since their conception, VGEs have attracted considerable attention in the geographic information science research community over the last few decades (e.g., Goodchild 2009;Huang et al 2018;Jia et al 2015;Konecny 2011;Liang et al 2015;Mekni 2010;Priestnall et al 2012;Rink et al 2018;Shen et al 2018;Torrens 2015;Zhang et al 2018;Zheng et al 2017). Much like the "digital twin" idea, and well-aligned with the Digital Earth concept, VGEs often aim to mirror realworld geographic environments in virtual ones.…”

Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

“…We opted to implement our own crowd simulation instead of using available crowd simulators or models, because the latter are built for different purposes. In other words, in crowd simulators it is challenging to implement high crowd density [35] and correlated groups of friends [38]; on the other hand, we are clearly not interested in the exact pattern of movement of the visitors, which is the focus of crowd simulatorsany positive effect from simulating visitors movement on a fine scale is flattened by the uncertainties of circa 20 m and the signal sparseness introduced by the Wi-Fi data.…”

Detecting high indoor crowd density with Wi-Fi localization: A statistical mechanics approach

“…We opted to implement our own crowd simulation instead of using available crowd simulators or models, because the latter are built for different purposes. In other words, in crowd simulators it is challenging to implement high crowd density [35] and correlated groups of friends [38]; on the other hand, we are clearly not interested in the exact pattern of movement of the visitors, which is the focus of crowd simulatorsany positive effect from simulating visitors movement on a fine scale is flattened by the uncertainties of circa 20m and the signal sparseness introduced by the Wi-Fi data.…”