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 …

Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in
mobility behavior with applications in domains like infectious disease monitoring and elderly …

Semantic trajectories, which enrich spatial-temporal data with textual information such as trip
purposes or location activities, are key for identifying outlier behaviors critical to healthcare …

Human mobility data science using trajectories or check-ins of individuals has many
applications. Recently, we have seen a plethora of research efforts that tackle these …

Understanding, analyzing, and predicting human mobility and dynamics are valuable to
solving pressing problems, develo** effective plans, and prescribing timely remedies. As …

We demonstrate the Patterns of Life Simulation to create realistic simulations of human
mobility in a city. This simulation has recently been used to generate massive amounts of …

Source localization aims to locate information diffusion sources only given the diffusion
observation, which has attracted extensive attention in the past few years. Existing methods …

Spatiotemporal data faces many analogous challenges to natural language text including
the ordering of locations (words) in a sequence, long range dependencies between …

Identifying travel modes is essential for modern urban transportation planning and
management. Recent advancements in data collection, especially those involving Global …

Human mobility anomaly detection based on location is essential in areas such as public
health, safety, welfare, and urban planning. Develo** models and approaches for location …