Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks（CTBNs） is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.
Shaojie QIAOXian WANGLu＇an TANGLiangxu LIUXun GONG
为了克服2G和3G移动通信网络位置管理方案的缺陷,4G长期演进(Long Term Evolution,LTE)采用了基于跟踪区列表(Tracking Area List,TAL)的位置管理方案。基于TAL的位置管理方案的性能取决于TAL分配方案。考虑到本地移动设备(User Equipment,UE)的活动区域相对固定,该文提出一种嵌入式马尔科夫链模型,用于分析本地UE的基于TAL的位置管理方案的信令开销。推导得到位置更新开销和寻呼开销的数学公式。利用这些公式,可搜索得到能使信令开销最低化的最佳TAL分配方案。
