← 研踪 PaperTrailarXiv:2606.10314 · v3
Synthesizing Human Trajectory Anomalies: An LLM-Driven Framework with Map-Constrained Routing and Sensor Noise Simulation
首次提交 2026-06-09 10:09
版本更新 2026-11-12 09:21
雷达观测 2026-11-13 14:16
摘要
Although the study of human trajectory anomalies is vital for spatial data mining, empirical research remains constrained by the lack of ground-truth datasets. Despite the availability of several real-world and simulated human trajectory datasets, most only include normal mobility patterns and lack annotated anomalies. This limitation primarily arises because anomalous events are rare in the real world and difficult to collect. Furthermore, collecting large-scale mobility data is hindered by strict privacy regulations and acquisition costs. To overcome these limitations and establish an explainable human trajectory anomalies dataset with annotated ground truth, we introduce an end-to-end generative framework designed to synthesize context-aware trajectory anomalies at scale. Unlike existing models, our framework applies real-world physical constraints 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 the spatial validity, the system leverages a map-constrained routing module 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 emulate heterogeneous GPS sensor degradation. Through this pipeline, our framework produces a CTI & EBI formatted mobility dataset with annotated anomalies, providing a benchmark for downstream spatial data mining tasks.
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v3 · 2026-11-12 09:21 · 最新观测版本
Synthesizing Human Trajectory Anomalies: An LLM-Driven Framework with Map-Constrained Routing and Sensor Noise Simulation
Although the study of human trajectory anomalies is vital for spatial data mining, empirical research remains constrained by the lack of ground-truth datasets. Despite the availability of several real-world and simulated human trajectory datasets, most only include normal mobility patterns and lack annotated anomalies. This limitation primarily arises because anomalous events are rare in the real world and difficult to collect. Furthermore, collecting large-scale mobility data is hindered by strict privacy regulations and acquisition costs. To overcome these limitations and establish an explainable human trajectory anomalies dataset with annotated ground truth, we introduce an end-to-end generative framework designed to synthesize context-aware trajectory anomalies at scale. Unlike existing models, our framework applies real-world physical constraints 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 the spatial validity, the system leverages a map-constrained routing module 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 emulate heterogeneous GPS sensor degradation. Through this pipeline, our framework produces a CTI & EBI formatted mobility dataset with annotated anomalies, providing a benchmark for downstream spatial data mining tasks.
查看此版本解读 →查看此版本原文 ↗v2 · 2026-11-09 04:37
Synthesizing End-to-End Human Trajectory Anomalies: An LLM-Driven Framework with Map-Constrained Routing and Sensor Noise Simulation
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 an end-to-end generative framework designed to synthesize context-aware 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 emulate heterogeneous GPS sensor degradation. Through this pipeline, the system generates systematically annotated mobility datasets that provide a standardized benchmark for evaluating downstream spatial data mining models.
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