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Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics. (arXiv:2211.09510v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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 Self-supervised
trajectory representation learning framework …
arxiv representation representation learning semantics temporal travel