Spatio-temporal human mobility prediction based on trajectory data mining for resource management in mobile communication networks
Spatio-temporal human mobility prediction based on trajectory data mining for resource management in mobile communication networks
复制标题
DOI：
10.1109/hpsr.2019.8808106
复制
发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Shingo Enami;K. Shiomoto
中科院分区：
文献类型：
--
作者：
Shingo Enami;K. Shiomoto
在未来的移动的通信中，期望基于各种移动模型的通信。在能够灵活地分配网络资源的5G移动的通信网络中，为了适当地分配网络资源，需要预测业务需求。因此，重要的是要预测时空移动性的行为，以便适当地分配网络资源。基于服务的移动的设备的普及导致时空数据集的数量不断增加，并且导致发现关于移动行为的可用知识的机会。该知识对于向期望增加业务流的移动的网络提供稳定的通信是有用的。在本文中，我们提出了一种方法来把握时空的移动性的行为，通过挖掘从GPS数据中获得的移动性的轨迹数据，以预测未来的移动性的用户从频繁模式。我们提出了一个挖掘和预测算法，采用了大量的轨迹数据。我们采用序列模式挖掘算法，包括前缀跨度和BIDE的轨迹数据库中获得频繁的轨迹模式。我们使用实际的轨迹数据集，Geolife项目评估所提出的方法，并证明所提出的方法成功地提取了足够数量的频繁轨迹模式来预测未来的移动轨迹。
In the future mobile communication, communication based on various mobility models is expected. In 5G mobile communication network that can flexibly allocate network resources, it is necessary to predict traffic demands in order to appropriately allocate network resources. Therefore, it is important to predict the behavior of spatio-temporal mobility in order to appropriately allocate network resources. The pervasiveness of mobile devices based services leading to an increasing volume of spatiotemporal datasets and to the opportunity of discovering usable knowledge about mobility behavior. This knowledge is useful to provide stable communication to mobile networks expected to increase traffic flow. In this paper, we propose a method to grasp the behavior of the mobility in spatio-temporal by mining the trajectory data of the mobility obtained from the GPS data to predict the future mobility of the user from frequent patterns. We propose a mining and prediction algorithm that employs the huge amount of trajectory data. We apply sequential pattern mining algorithms including PrefixSpan and BIDE to obtain frequent trajectory patterns from trajectory database. We evaluate the proposed method using actual trajectory dataset, Geolife project, and demonstrate that the proposed method successfully extracts sufficient number of frequent trajectory patterns to predict the future trajectory of mobility.