Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints
Quick Answer
This study presents a novel generative framework that uses Large Language Models to synthesize realistic human trajectory anomalies while adhering to kinematic constraints.
Quick Take
This study presents a novel generative framework that uses Large Language Models to synthesize realistic human trajectory anomalies while adhering to kinematic constraints. By leveraging baseline simulated trajectories and a context-aware spatial noise model, the framework addresses the lack of annotated anomaly datasets, crucial for advancing spatial data mining.
Key Points
- Introduces an end-to-end generative framework for synthesizing trajectory anomalies.
- Uses LLM agents to inject meaningful behavioral anomalies into simulated trajectories.
- Employs map-constrained routing to ensure spatial validity of generated anomalies.
- Augments trajectories with a context-aware spatial noise model for realism.
- Addresses the scarcity of annotated datasets in human trajectory anomaly research.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 10314v1 Announce Type: new Abstract: 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 several real-world and simulated human trajectory collections, these datasets exclusively capture normal mobility patterns and lack annotated anomalies.
This specific scarcity is fundamentally driven by the inherent statistical rarity of anomalous events, precluding the feasibility of conventional observational methods. Compounding this challenge, the systematic acquisition of large-scale mobility data is strictly bottlenecked by prohibitive costs and stringent privacy regulations.
To overcome these fundamental limitations and establish a reliable human trajectory anomalies dataset with annotated ground truth, we introduce a novel, end-to-end generative framework designed to synthesize realistic trajectory anomalies at scale. Our architecture bridges the gap between purely synthetic mobility data and complex real-world physical constraints by operating directly on baseline simulated trajectories.
We employ Large Language Model (LLM) agents to systematically inject semantically meaningful behavioral anomalies such as irregular out-of-distribution check-ins and skipped routine visits. To ensure rigorous spatial validity, the system leverages map-constrained routing reconstruction to recalculate the physical transitions between these LLM agent-modified staypoints.
Moreover, to narrow the simulation-to-reality gap, we augment the resulting trajectories with a context-aware spatial noise model, parameterized by environmental and location-specific variables, to accurately emulate heterogeneous GPS sensor degradation.
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