A Fast and Accurate Index Structure for Spatiotemporal Trajectories

Abstract

There are many applications that rely on the analysis of historical trajectory data provided by location-aware devices. Traffic management, ridesharing and Pay-As-You-Drive insurance are some example applications. These applications require one to scan a large-scale historical dataset in order to extract similar trajectories to a query trajectory. A naive approach is a one-by-one similarity comparison between the query trajectory and dataset trajectories. However, most existing methods for assessing similarity between pairs of trajectories have quadratic time-complexity, making sequential scan of a large dataset computationally expensive. Existing solutions for this problem mainly focus on reducing the computation time by summarizing the trajectories or by defining upper-bounds (lower-bounds) for the similarity between two trajectories. However, these solutions do not directly address the efficiency challenge. To tackle this challenge of large scale similar trajectory retrieval, we propose an efficient index structure that filters out distant trajectories and outputs an approximate set of similar trajectories. Experimental results show that our novel index structure significantly improves the efficiency of trajectory retrieval, whilst maintaining high accuracy.