2 citations · 2 across the 4 of their papers we have counts for
4 papers
BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Junyi Xie, Jina Kim, Yao-Yi Chiang +2
Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggreg…
HiCoTraj:Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory
Junyi Xie, Yuankun Jiao, Jina Kim +3
Inferring demographic attributes such as age, sex, or income level from human mobility patterns enables critical applications such as targeted public health interventions, equitabl…
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework
Minxuan Duan, Yinlong Qian, Lingyi Zhao +4
Existing methods for anomaly detection often fall short due to their inability to handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data…
NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks
Chris Stanford, Suman Adari, Xishun Liao +10
Collecting real-world mobility data is challenging. It is often fraught with privacy concerns, logistical difficulties, and inherent biases. Moreover, accurately annotating anomali…