Trajectory computing is a pivotal domain encompassing trajectory data management and
mining, garnering widespread attention due to its crucial role in various practical …

Spatio-temporal trajectory analytics are useful in diversified applications such as urban
planning, infrastructure development, and vehicular networks. Trajectory similarity measure …

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

Trajectories that capture object movement have numerous applications, in which similarity
computation between trajectories often plays a key role. Traditionally, trajectory similarity is …

The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Trajectory similarity computation serves as a fundamental functionality of various spatial
information applications. Although existing deep learning similarity computation methods …

With the increasing popularity of automatic identification system AIS devices, mining latent
vessel motion patterns from AIS data has become a hot topic in water transportation …

Cardinality estimation, which involves estimating the result size of queries, is a critical aspect
of query processing and optimization. Deep Neural Networks (DNNs) are data hungry, and …

Searching for similar trajectories from a database is an important way for extracting human-
understandable knowledge. However, due to the huge volume of trajectories and high …

Trajectory similarity computation is a fundamental function in many applications of urban
data analysis, such as trajectory clustering, trajectory compression, and route planning. In …