TRAJEDI: Trajectory Dissimilarity

TL;DR

TRAJEDI introduces a calibration-aware trajectory distance method that improves speed and accuracy for large datasets, enhancing trajectory analytics operations like similarity searches and k-nearest neighbors.

Contribution

It proposes a novel calibration-aware distance calculation scheme that outperforms naive methods and is adaptable to various trajectory analysis algorithms.

Findings

Outperforms naive approaches in speed and accuracy

Effective parameter tuning for speed-accuracy trade-offs

Compatible with diverse trajectory analysis algorithms

Abstract

The vast increase in our ability to obtain and store trajectory data necessitates trajectory analytics techniques to extract useful information from this data. Pair-wise distance functions are a foundation building block for common operations on trajectory datasets including constrained SELECT queries, k-nearest neighbors, and similarity and diversity algorithms. The accuracy and performance of these operations depend heavily on the speed and accuracy of the underlying trajectory distance function, which is in turn affected by trajectory calibration. Current methods either require calibrated data, or perform calibration of the entire relevant dataset first, which is expensive and time consuming for large datasets. We present TRAJEDI, a calibrationaware pair-wise distance calculation scheme that outperforms naive approaches while preserving accuracy. We also provide analyses of parameter tuning to trade-off between speed and accuracy. Our scheme is usable with any diversity, similarity or k-nearest neighbor algorithm.