NEIST: a Neural-Enhanced Index for Spatio-Temporal Queries

Abstract

Previous work on the spatial-temporal index often adopts a simple linear model to predict the future positions of moving objects, which may generate numerous errors for complex road networks and fast moving objects. In this paper, we propose NEIST, a neural-enhanced index to process spatial-temporal queries with enhanced efficiency and accuracy, by intelligently leveraging the movement patterns among moving objects. NEIST applies a Recurrent Neural Network (RNN) model to predict future positions of moving objects based on observed trajectories. To reduce the prediction overhead, a suffix-tree is further built to index trajectories with similar suffixes, and thus similar objects within a given similarity bound are grouped together to share the same prediction result. A prediction result in NEIST represents possible positions of a group of moving objects in the next t time slots. Inside each time slot, traditional linear prediction model is then adopted and a TPR-Tree is built to support spatial-temporal queries. We use Singapore taxi trajectory dataset collected over one whole month to evaluate NEIST. Compared to previous approaches, NEIST achieves a much more efficient query performance and is able to produce about 30% more accurate results.