Recent advances in sensor and mobile devices have enabled an unprecedented increase
in the availability and collection of urban trajectory data, thus increasing the demand for …

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

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

Computing trajectory similarities is a critical and fundamental task for various spatial-
temporal applications, such as clustering, prediction, and anomaly detection. Traditional …

With the increasing adoption of GPS modules, there are a wide range of urban applications
based on trajectory data analysis, such as vehicle navigation, travel time estimation, and …

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 …

The growing number of cars on city roads has led to an increase in traffic accidents,
highlighting the need for traffic safety measures. Mobile trajectory anomaly detection is an …

In this paper, we study anomalous trajectory detection, which aims to extract abnormal
movements of vehicles on the roads. This important problem, which facilitates understanding …

Map matching is a fundamental research topic with the objective of aligning GPS trajectories
to paths on the road network. However, existing models fail to achieve satisfactory …

Human trajectory anomaly detection is critical for applications such as security surveillance
and public health, yet most existing methods focus on vehicle-level traffic, with limited …