A Hybrid Aggregate Index Method for Trajectory Data

The aggregate query of moving objects on road network keeps being popular in the ITS research community. The existing methods often assume that the sampling frequency of the positioning devices like GPS or roadside radar is dense enough, making the result's uncertainty negligible. However, such assumption is not always tenable, especially in the extreme occasions like wartime. Regarding this issue, a hybrid aggregate index framework is proposed in this paper, in order to perform aggregate queries on massive trajectories that are sampled sparsely. Firstly, this framework uses an offline batch processing component based on the UPBI-Sketch index to acquire each object's most likely position between two continuous sampling instants. Next, it introduces the AMH(+)-Sketch index to processing the aggregate operation online, making sure each object is counted only once in the result. The experimental results show that the hybrid framework can ensure the query accuracy by adjusting the parameters L and U of AMH(+)-Sketch index and its space storage advantage becomes more and more obvious when the data scale is very large.