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 …

In this work, we propose a robust road network representation learning framework called
Toast, which comes to be a cornerstone to boost the performance of numerous demanding …

Deep learning based trajectory similarity computation holds the potential for improved
efficiency and adaptability over traditional similarity computation. However, existing learning …

Informative representation of road networks is essential to a wide variety of applications on
intelligent transportation systems. In this article, we design a new learning framework, called …

Trajectory data is essential for various applications. However, publicly available trajectory
datasets remain limited in scale due to privacy concerns, which hinders the development of …

Trajectory prediction of vehicles in city-scale road networks is of great importance to various
location-based applications such as vehicle navigation, traffic management, and location …

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

Road network and trajectory representation learning are essential for traffic systems since
the learned representation can be directly used in various downstream tasks (eg, traffic …

The computation of trajectory similarity is a crucial task in many spatial data analysis
applications. However, existing methods have been designed primarily for trajectories in …