Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics

arXiv:2211.09510v4 Announce Type: replace
Abstract: Trajectory Representation Learning (TRL) is a powerful tool for spatial-temporal data analysis and management. TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation. Existing TRL works usually treat trajectories as ordinary sequence data, while some important spatial-temporal characteristics, such as temporal regularities and travel semantics, are not fully exploited. To fill this gap, we propose a novel …

abstract analysis arxiv classification clustering computation cs.lg data data analysis low management raw representation representation learning semantics spatial tasks temporal tool trajectory travel type vectors