Robust and fast similarity search for moving object trajectories

TLDR

Similarity‑based retrieval of moving object trajectories relies on a distance function, yet existing functions are sensitive to noise, shifts, scaling, and varying sampling rates, and data cleaning is often infeasible. The authors propose a new distance function, Edit Distance on Real sequence (EDR), designed to be robust against such data imperfections. They also devise three pruning techniques that can be combined to substantially increase retrieval efficiency for EDR. Experiments show that EDR is more robust than Euclidean distance, DTW, and ERP, about 50 % more accurate than LCSS, and that the combined pruning methods yield superior efficiency.

Abstract

An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imperfections. Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning techniques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increasing the pruning power significantly. The experimental results confirm the superior efficiency of the combined methods.