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 …

GPS trajectories are the essential foundations for many trajectory-based applications. Most
applications require a large number of high sample rate trajectories to achieve a good …

Computing trajectory similarity plays a critical role in various spatio-temporal applications
that involve trajectory analysis. In recent years, trajectory representation learning has been …

The proliferation of geospatial data in urban and territorial environments has significantly
facilitated the development of geospatial artificial intelligence (GeoAI) across various urban …

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 …

Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the
constraints of the road network, could offer deep insights into users' moving behaviors in …

In the realm of human mobility data analysis, a multitude of constraints result in the
publication of sparse, non-uniform implicit trajectories without explicit location information …

Machine learning-based forecasting models are commonly used in Intelligent Transportation
Systems (ITS) to predict traffic patterns and provide city-wide services. However, most of the …

The dynamic correlation analysis of cell-spatial information (distributed anode current signal,
DACS) is of great significance in the regional-refined control of industrial aluminum …