TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
TrajGenAgent proposes a hierarchical LLM agent framework for generating realistic synthetic human mobility trajectories without model fine-tuning. It uses a two-stage orchestrator-worker design: an LLM first synthesizes individual- and weekday-conditioned activity chains via in-context learning, then a deterministic workflow grounds each activity into a complete visit using personalized POI retrieval, distance-aware location selection, kinematics-aware travel-time propagation, and LLM-based duration estimation. An anomaly-detection-based evaluation framework assesses behavioral and semantic plausibility. Experiments show improvements in spatiotemporal fidelity, semantic coherence, and individual-specific behavioral realism over existing methods.
[2606.12657] TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
[Submitted on 10 Jun 2026]
Title:TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
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Abstract:Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation. Existing LLM-based generators typically rely on either prompt engineering, which preserves zero-shot reasoning but lacks fine-grained spatiotemporal grounding, or trajectory-level fine-tuning, which improves statistical precision but incurs substantial computational cost and may weaken general reasoning. We propose TrajGenAgent, a semantic-aware hierarchical LLM-agent framework for human mobility trajectory generation without model fine-tuning. TrajGenAgent uses a two-stage orchestrator-worker design: an LLM first synthesizes an individual- and weekday-conditioned activity chain from historical evidence via in-context learning, and a deterministic workflow then grounds each activity into a complete visit using personalized POI retrieval, distance-aware location selection, kinematics-aware travel-time propagation, and LLM-based duration estimation. To evaluate realism beyond aggregate spatiotemporal statistics, we introduce an anomaly-detection-based evaluation framework using two complementary detectors to assess behavioral and semantic plausibility. Experiments on benchmark and large-scale simulation datasets show that TrajGenAgent improves spatiotemporal fidelity, semantic coherence, and individual-specific behavioral realism over representative neural and LLM-based baselines, while avoiding parameter updates.
Comments: 14 pages, 2 figures, 8 tables. Accepted by the 27th IEEE International Conference on Mobile Data Management (MDM 2026)
Subjects:
Artificial Intelligence (cs.AI); Databases (cs.DB); Robotics (cs.RO)
Cite as: arXiv:2606.12657 [cs.AI]
(or arXiv:2606.12657v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12657
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Siyu Li [view email] [v1] Wed, 10 Jun 2026 20:32:52 UTC (3,329 KB)
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