Clustering of Mountain Hiking GPS Trajectory Data

Co-first author · Under Revision at IEEE Access

An empirical study proposing a novel trajectory similarity measure for irregular spatio-temporal GPS data.

Proposed a novel trajectory similarity measure integrating spatial, temporal, and velocity components for irregular spatio-temporal data.
Designed an ε-ball-based spatial similarity metric and a data-adaptive weighting framework (α, β, γ) for flexible similarity modeling.
Applied hierarchical clustering to large-scale GPS trajectory data to uncover latent behavioral patterns.

(Back et al., 2026)

References

2026

IEEE Access
An Empirical Study of the Clustering of Mountain Hiking GPS Trajectory Data

SeungYeon Back, Manhwi Han, Dong-Kyu Chae, and 1 more author

IEEE Access, 2026

Under revision; Co-first author

Abs Bib

We propose a novel trajectory similarity measure integrating spatial, temporal, and velocity components for irregular spatio-temporal data, along with an epsilon-ball-based spatial similarity metric and a data-adaptive weighting framework (alpha, beta, gamma). Hierarchical clustering is applied to large-scale GPS trajectory data to uncover latent behavioral patterns.
@article{gpshiking2026, title = {An Empirical Study of the Clustering of Mountain Hiking GPS Trajectory Data}, author = {Back, SeungYeon and Han, Manhwi and Chae, Dong-Kyu and Park, Seoncheol}, journal = {IEEE Access}, year = {2026}, note = {Under revision; Co-first author}, }