一种基于受限网络的移动对象索引

Indexing of Moving Objects in a Constrained Network

为了有效地支持城市交通网络中移动对象的过去、现在和将来的轨迹查询,在基于模拟预测的位置表示模型基础上,提出了一种两层R树加上一个表结构的复合索引结构AUC(Adaptive Unit Compounding)。根据城市交通网的特征,采用了一种带有环形交叉口的元胞自动机模型模拟移动对象的将来轨迹,并用线性回归和圆弧曲线拟合分别得到对象在规则路段和交叉口的轨迹预测方程;根据移动对象的运动特性,采用了一种新的自适应单元(AU)作为索引结构的基本单位。实验表明,AUC索引的查询和更新性能都要优于TPR树和TB树。

Advance in wireless sensor networks and positioning technologies enable new data management applications to monitor continuous streaming data. An efficient indexing structure for moving objects is necessary for supporting the query processing of these dynamic data. This paper proposed a new index technique based on a simulation prediction model,which supported querying the past, present and future positions of moving objects in urban traffic networks. First, making full use of the feature of urban traffic networks, we used cellular automata model with crossings to simu-late the movements of the objects. Then, by linear regression and circular arc fragmented curve-fitting, the prediction trajectory equation of the objects in regular road segment and in crossing could be obtained. Moreover, we presented a dynamic structure named AU（adaptive units） which grouped neighbor objects moving in the similar moving patterns and developed it a two levels R-tree and a link list based index named AUC（Adaptive Unit Compounding） index. Finally, experimental studies indicated that the AUC index outperformed TPR-tree and TB-tree.