  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.
