Large Language Models for Spatial Trajectory Patterns Mining
Zheng Zhang Hossein Amiri Zhenke Liu Andreas Züfle Liang Zhao
Identifying anomalous human spatial trajectory patterns can indicate dynamic
changes in mobility behavior with applications in domains like infectious
disease monitoring and elderly care. Recent advancements in large language
models (LLMs) have demonstrated their ability to reason in a manner akin to
humans. This presents significant potential for analyzing temporal patterns in
human mobility. In this paper, we conduct empirical studies to assess the
capabilities of leading LLMs like GPT-4 and Claude-2 in detecting anomalous
behaviors from mobility data, by comparing to specialized methods. Our key
findings demonstrate that LLMs can attain reasonable anomaly detection
performance even without any specific cues. In addition, providing contextual
clues about potential irregularities could further enhances their prediction
efficacy. Moreover, LLMs can provide reasonable explanations for their
judgments, thereby improving transparency. Our work provides insights on the
strengths and limitations of LLMs for human spatial trajectory analysis.