DHB - Modeling in Social Dynamics: A Differential Approach

本研究提出了基于Helbing-Moln ar-Farkas-Vicsek （HMFV）行人运动模型的行人运动模拟的主要扩展。这个模型结合了社会和物理力量来确定模型中的个体如何移动和互动。这种扩展的目的是将年龄、性别、种族和文化差异的特征折叠到模型中，以便能够捕获由于这四个因素而产生的可靠差异。这样的结果可以用来提高我们模拟社会动态的能力。这将通过赋予模拟中的每个个体与文化、种族、性别和年龄相关的某些特征和特征来实现。然后，模拟将展示这些个体特征的变化如何影响个体行为和群体行为，就像在人群中一样。这四个因素（文化，种族，性别和年龄）将提供足够的空间让游戏变得有趣。然而，也有一些有趣的情况，人们想要对具有相似特征的个体群体进行建模。首先应该集中在这些方面，随着经验的积累，扩大空间的维度。这些特征在模拟人群中的分布将是随机的，以便能够灵活地模拟各种情况（例如夜总会火灾、体育赛事、地铁站台）。拥有特征的随机分布将使我们有机会确定整体群体行为对特征分布的微小变化有多敏感。它还允许我们模拟“最坏情况”，以便那些从事人群控制（或建筑设计）的人可以为最不利的结果做好准备。最近的世界事件促使人们研究在夜总会火灾、体育场事故和地铁爆炸等紧急情况下人群的行为。全球恐怖主义的持续威胁和大规模恐慌的可能性可能导致更多被碾压或践踏的人死亡。这个项目将使用数学建模和仿真来构建不同情况下行人相互作用的模型。结果将使用从不同场景的视频中获得的实际人群移动数据进行验证。研究结果将有助于理解在各种条件下社会变异性的影响和重要性。这项工作的目标是通过考虑年龄、性别、文化和种族的个体差异，提供一个更真实的恐慌和非恐慌条件下人群行为的模拟模型。这将是警察部门、军队和其他部门在事件处理或恐怖主义情况下使用的宝贵工具。它也可以用于建筑设计，以适应文化差异。

NSF ABSTRACT for:MODELING IN SOCIAL SYNAMICS: A DIFFERENTIAL APPROACHThis research proposes a major expansion of a simulation of pedestrian motion based on the model of pedestrian motion by Helbing-Moln ar-Farkas-Vicsek (HMFV). This model uses a combination of social and physical forces to determine how the individuals in the model move and interact. The purpose of this expansion is to fold characteristics concerning age, gender, ethnicity, and culture differences into the model, so as to be able to capture reliable differences due to these four factors. Such results could then be used to improve our ability to model social dynamics. This will be done by giving each individual in the simulation, certain traits and characteristics, related to culture, ethnicity, gender, and age. The simulations would then demonstrate how these variations in individual characteristics can affect individual behavior and group behavior, as in a crowd. These four factors (culture, ethnicity, gender, and age) will certainly provide a sufficient space to be interesting. However, there are also interesting cases wherein one would want to model groups of individuals with similar characteristics. These shall be concentrated on at first, expanding the dimension of the space as experience is gained. The distribution of these traits in the simulated populations will be stochastic so as to have flexibility to model a variety of situations (e.g. night club fire, a sporting event, a subway platform). Having stochastic distributions of traits will allow the opportunity to determine how sensitive the overall crowd behavior is to small changes in distributions of the traits. It will also allow us to model "worst case" scenarios so that those engaged in crowd control (or building design) can prepare for the most detrimental outcome.Recent world events prompt research in understanding crowd behavior in emergencies such as nightclub fires, stadium accidents, and subway bombings. The continual threat of global terror and the potential for mass panic could lead to more fatalities of people who are crushed or trampled. This project will use mathematical modeling and simulations to construct models of pedestrian interactions under various situations. The results will be validated using actual crowd movement data obtained from videos from various scenarios. The findings will provide understanding of the influence and importance of social variability under various conditions. The goal of this effort is to provide a more realistic simulation model of crowd behavior under both panic and non-panic conditions by considering individual differences in age, gender, culture and ethnicity. This would be a valuable tool for police departments, the military, and others to use for event handling or in terrorist situations. It could also be used for building design to accommodate cultural differences.