Spatio-temporal trajectories provide valuable information about movement and travel
behavior, enabling various downstream tasks that in turn power real-world applications …

Pre-training trajectory embeddings is a fundamental and critical procedure in spatial-
temporal trajectory mining, and is beneficial for a wide range of downstream tasks. The key …

Trajectory representation learning aims to embed trajectory sequences into fixed-length
vector representations while preserving their original spatio-temporal feature proximity …

Trajectory mining has gained growing attention due to its emerging applications, such as
location-based services, urban computing, and movement behavior analyses. One critical …

In this article, we propose a novel representation learning framework, namely TRajectory
EMBedding via Road networks (Trembr), to learn trajectory embeddings (low-dimensional …

Trajectory Representation Learning (TRL) is a powerful tool for spatial-temporal data
analysis and management. TRL aims to convert complicated raw trajectories into low …

Trajectory prediction is a crucial undertaking in understanding entity movement or human
behavior from observed sequences. However, current methods often assume that the …

Advancements in mobile sensing, data mining, and artificial intelligence have revolutionized
the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the …

Abstract Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media
(GTSM) applications, enabling personalized Point of Interest (POI) recommendation and …

Most of the existing literature regarding hyperbolic embedding concentrate upon supervised
learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In …