Large Language Models for Spatial Trajectory Patterns Mining

2023-10-07 · 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.

📄 PDF Abstract BibTeX arXiv:2310.04942

Code (0)

등록된 구현이 없습니다.

Tasks

Anomaly Detection

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Position-Wise Feed-Forward Layer 설명 없음

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…

Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

Adam 설명 없음

Pattern Ensembling for Spatial Trajectory Reconstruction

2021-01-25 · Shivam Pathak, Mingyi He, Sergey Malinchik, Stanislav Sobolevsky

Digital sensing provides an unprecedented opportunity to assess and understand mobility. However, incompleteness, missing information, possible inaccuracies, and temporal heterogeneity in the geolocation data can undermi…

Semantic Trajectory Data Mining with LLM-Informed POI Classification

2024-05-20 · Yifan Liu, Chenchen Kuai, Haoxuan Ma, Xishun Liao 외

Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of se…

Management

Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints

2026-06-09 · Yueyang Liu, Joon-Seok Kim, Andreas Züfle arxiv

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 datasets. Despite the availability of sever…

SMc2f: Robust Scenario Mining for Robotic Autonomy from Coarse to Fine

2026-01-17 · Yifei Chen, Ross Greer arxiv

The safety validation of autonomous robotic vehicles hinges on systematically testing their planning and control stacks against rare, safety-critical scenarios. Mining these long-tail events from massive real-world drivi…

Contrastive Learning 3D Object Detection Video Retrieval

Understanding Trajectory Behavior: A Motion Pattern Approach

2015-01-04 · Mahdi M. Kalayeh, Stephen Mussmann, Alla Petrakova, Niels da Vitoria Lobo 외

Mining the underlying patterns in gigantic and complex data is of great importance to data analysts. In this paper, we propose a motion pattern approach to mine frequent behaviors in trajectory data. Motion patterns, def…

Clustering Trajectory Clustering