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A Mobility Model of Theme Park Visitors

机译:主题公园游客的流动模型

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Realistic human mobility modeling is critical for accurate performance evaluation of mobile wireless networks. Movements of visitors in theme parks affect the performance of systems which are designed for various purposes including urban sensing and crowd management. Previously proposed human mobility models are mostly generic while some of them focus on daily movements of people in urban areas. Theme parks, however, have unique characteristics in terms of very limited use of vehicles, crowd’s social behavior, and attractions. Human mobility is strongly tied to the locations of attractions and is synchronized with major entertainment events. Hence, realistic human mobility models must be developed with the specific scenario in mind. In this paper, we present a novel model for human mobility in theme parks. In our model, the nondeterminism of movement decisions of visitors is combined with deterministic behavior of attractions in a theme park. The attractions are categorized as rides, restaurants, and live shows. The time spent at these attractions are computed using queueing-theoretic models. The realism of the model is evaluated through extensive simulations and compared with the mobility models SLAW, RWP and the GPS traces of theme park visitors. The results show that our proposed model provides a better match to the real-world data compared to the existing models.
机译:现实的人类移动性建模对于准确评估移动无线网络的性能至关重要。主题公园中游客的活动会影响系统的性能,这些系统是为各种目的而设计的,包括城市感知和人群管理。先前提出的人员流动模型大多是通用的,而其中一些模型则关注城市地区人们的日常活动。但是,主题公园在限制使用车辆,人群的社交行为和景点方面具有独特的特征。人的流动性与景点的位置紧密相关,并且与大型娱乐活动保持同步。因此,必须在考虑特定场景的情况下开发现实的人员流动模型。在本文中,我们提出了主题公园中人类活动的新型模型。在我们的模型中,游客的运动决策的不确定性与主题公园中景点的确定性行为相结合。景点分为游乐设施,餐厅和现场表演。使用排队理论模型计算在这些景点上花费的时间。该模型的真实性通过广泛的仿真进行了评估,并与SLAW,RWP和主题公园游客的GPS追踪模型进行了比较。结果表明,与现有模型相比,我们提出的模型可以更好地匹配实际数据。

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