TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

• URL: http://arxiv.org/abs/2606.12657v1
• Date: Wed, 10 Jun 2026 20:32:52 GMT
• Title: TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
• Abstract summary: Large-scale trajectory collection is often costly and privacy-constrained.<n>Existing trajectory generators rely on either prompt engineering, which lacks fine-grained reasoning, or trajectory-level fine-tuning.<n>We propose TrajGenAgent, a semantic-aware framework for trajectory generation without model-tuning.
• Score: 7.748185086458265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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