Location graphs, compact representations of human mobility without geocoordinates, can be
used to personalise location-based services. While they are more privacy-preserving than …

Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate
internal workings pose challenges for interpretability, especially in comprehending how …

Cyber-Physical-Social Systems (CPSS) offer a new perspective for applying advanced
information technology to improve urban transportation. However, real-world traffic datasets …

The growing ubiquity of location/activity sensing technologies has created unprecedented
opportunities for research on human spatiotemporal interaction behavior in mobile …

Large‐scale trajectory data offer a finer lens into the regularity in individual mobility choices.
Previous studies have exerted efforts to measure the regularity in people's location visiting …

This paper presents a comprehensive agent-based model for the spread of an infection in a
network of cities. Directional mobility is defined between each two cities and can take …

Data captured by mobile devices enable us, among other things, learn the places where
users go, identify their home and workplace, the places they usually visit (eg, supermarket …

Human mobility exhibits power-law distributed visitation patterns; ie, a few locations are
visited frequently and many locations only once. Current research focuses on the important …

It is important to capture passengers' public transit behavior and their mobility to create
profiles, which are critical for analyzing human activities, understanding the social and …

This paper presents the Spatial Nudging framework-a theory-based framework that maps
out nudging strategies in the mobility domain and refines its existing definitions. We link …