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Discovery of stay area in indoor trajectories of moving objects

机译:在移动物体的室内轨迹中发现住宿区

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With the rapid development of location-based services, a large amount of moving object trajectory data has been generated. Mining users? stay area from moving trajectory data can not only improve location-based service, but also take precautions to ensure personal safety to better promote the development of location-based services. However, the research on trajectory of indoor moving objects is still in its infancy, and there is a lack of relevant research in this field. It is urgent to meet the needs of the location service industry. We have done some novel work on the stay area in indoor trajectories. Firstly, a novel algorithm named SVBDSA is proposed for the sta-bility value-based discovery of stay area. The stability value is calculated from the distance and velocity between the two points. By comparing the stability value with the threshold value, the two points are judged whether they are in a stable state, and then the point in the stable state is extended. Thus, the stay area in the moving tra-jectories can be discovered. Secondly, this paper proposes an improved algorithm named T -OPTICS, which adds the time attribute and puts forward the concept of difference value, so that abnormal stay points can be detected in the discovered stay area. Finally, the trajectory generation tool Vita is used to conduct experiments and analysis and the effectiveness of the proposed algorithms in this paper is verified. Discovery of stay area is not only a new and difficult scientific issue that has not been studied in the field of indoor trajectories of the moving object, but also has important academic value and research significance in location-based service industry.
机译:随着基于位置的服务的快速发展,已经生成了大量的移动物体轨迹数据。采矿用户?从移动轨迹数据中停留地区不仅可以改善基于位置的服务,还要采取预防措施,以确保人们的安全,更好地促进基于位置的服务的发展。然而,对室内移动物体轨迹的研究仍处于初期阶段,并且在这一领域缺乏相关的相关研究。迫切需要满足地理位置服务行业的需求。我们在室内轨迹的住宿地区做了一些新颖的工作。首先,提出了一种名为SVBDSA的新型算法,用于停留区域的STA Bility值的发现。稳定值由两点之间的距离和速度计算。通过将稳定值与阈值进行比较,判断它们是否处于稳定状态,然后延长稳定状态的点。因此,可以发现移动TRA jectories中的停留区域。其次,本文提出了一种名为T -Optics的改进算法,该算法添加了时间属性并提出了差值的概念,从而可以在发现的停留区域中检测到异常停留点。最后,轨迹生成工具VITA用于进行实验和分析,并验证了本文中所提出的算法的有效性。停留地区的发现不仅是一种新的和艰难的科学问题,尚未在移动物体的室内轨迹领域中学,而且在基于位置的服务业中也具有重要的学术价值和研究意义。

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