  Spatio-temporal (ST) trajectories are sequences of timestamped locations,
which enable a variety of analyses that in turn enable important real-world
applications. It is common to map trajectories to vectors, called embeddings,
before subsequent analyses. Thus, the qualities of embeddings are very
important. Methods for pre-training embeddings, which leverage unlabeled
trajectories for training universal embeddings, have shown promising
applicability across different tasks, thus attracting considerable interest.
However, research progress on this topic faces two key challenges: a lack of a
comprehensive overview of existing methods, resulting in several related
methods not being well-recognized, and the absence of a unified pipeline,
complicating the development new methods and the analysis of methods.
  To overcome these obstacles and advance the field of pre-training of
trajectory embeddings, we present UniTE, a survey and a unified pipeline for
this domain. In doing so, we present a comprehensive list of existing methods
for pre-training trajectory embeddings, which includes methods that either
explicitly or implicitly employ pre-training techniques. Further, we present a
unified and modular pipeline with publicly available underlying code,
simplifying the process of constructing and evaluating methods for pre-training
trajectory embeddings. Additionally, we contribute a selection of experimental
results using the proposed pipeline on real-world datasets.
