一种基于城市交通网络的移动对象全时态索引

高效地管理移动对象以支持查询是一个重要课题.为了支持在城市交通网络上的移动对象过去、现在和将来位置查询,提出了一种新的索引技术.首先提出基于模拟预测的位置表示模型来改进对移动对象将来运动轨迹的预测精度;其次根据城市交通网的特征,设计了一种全新的动态结构自适应单元(AU),将其开发为一个基于R树的索引结构(current-Au);最后在AU的基础上进行扩展(past-AU)使其支持移动对象历史轨迹查询并且避免了大量的死空间.实验证明,AU索引优于传统的TPR树和TB树索引.

Indexing the Past,Present and Future Positions of Moving Objects in Urban Traffic Networks

Advance in wireless sensor networks and positioning technologies enable new data management applications that monitor continuous streaming data. In these applications, efficient management of such data is a challenging goal due to the highly dynamic nature of the data and the need for fast, on-line computations. An efficient indexing structure for moving objects is necessary for supporting the query processing of these dynamic data. Existing work can not index the past, current and future positions of moving objects at the same time. In this paper, a novel index technique is proposed to support querying the past, present and future positions of moving objects in urban traffic networks. First, a simulation based location prediction model for the vehicle future trajectory is presented, which is more accurate than the traditional linear prediction model in the TPR-tree. Moreover, exploiting the feature of traffic networks, it presents a dynamic structure termed AU (adaptive unit) and develops it to an R-tree based index named current-AU. Finally, by naturally extending the AU, the past-AU is proposed, which is capable of indexing historical trajectory and at the same time avoiding the dead space that is inevitable in the TB-tree. Experimental studies indicate that the AU-index outperforms the traditional TPR-tree and TB-tree.