Time Window Frechet and Metric-Based Edit Distance for Passively Collected Trajectories

TL;DR

This paper introduces new similarity measures for passively collected human mobility trajectories, enabling clustering and analysis of group movement patterns, and provides complexity results for related clustering problems.

Contribution

It proposes the time-window Frechet and metric-based edit distances, and analyzes their computational complexity in trajectory clustering.

Findings

Proposed time-window Frechet and metric-based edit distances.

Proved NP-hardness of k-gather clustering under these distances.

Established lower bounds on approximation algorithms for discrete Frechet distance.

Abstract

The advances of modern localization techniques and the wide spread of mobile devices have provided us great opportunities to collect and mine human mobility trajectories. In this work, we focus on passively collected trajectories, which are sequences of time-stamped locations that mobile entities visit. To analyse such trajectories, a crucial part is a measure of similarity between two trajectories. We propose the time-window Frechet distance, which enforces the maximum temporal separation between points of two trajectories that can be paired in the calculation of the Frechet distance, and the metric-based edit distance which incorporates the underlying metric in the computation of the insertion and deletion costs. Using these measures, we can cluster trajectories to infer group motion patterns. We look at the $k$-gather problem which requires each cluster to have at least $k$ trajectories. We prove that k-gather remains NP-hard under edit distance, metric-based edit distance and Jaccard distance. Finally, we improve over previous results on discrete Frechet distance and show that there is no strongly sub-quadratic time with approximation factor less than $1.61$ in two dimensional setting unless SETH fails.