2 citations · 2 across the 5 of their papers we have counts for
5 papers
Staypoint Detection from Noisy Trajectory Data [Experiment Paper]
Lance Kennedy, Hossein Amiri, Yueyang Liu +9
Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantic…
Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints
Yueyang Liu, Joon-Seok Kim, Andreas Züfle
Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervasive lack of ground-truth dat…
SF-LIFE: A Large-Scale Simulated Movement Dataset for the San Francisco Bay Area
Chanuka Algama, Taylor Anderson, Henrique Ferraz de Arruda +14
We introduce SF-LIFE, a large-scale simulated movement dataset designed to accelerate research in transportation, mobility, and machine learning. The dataset contains 3,024,000,000…
Training Machine Learning Models on Human Spatio-temporal Mobility Data: An Experimental Study [Experiment Paper]
Yueyang Liu, Lance Kennedy, Ruochen Kong +2
Individual-level human mobility prediction has emerged as a significant topic of research with applications in infectious disease monitoring, child, and elderly care. Existing stud…
Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories
Yueyang Liu, Lance Kennedy, Hossein Amiri +1
Human trajectory anomaly detection has become increasingly important across a wide range of applications, including security surveillance and public health. However, existing traje…