用大模型驱动的智能体模拟人群行为，让轨迹分析更真实、易用。
SenseWalk: Agent-Based Semantic Trajectory Simulation Powered by Large Language Models in Zoned Environments
- 大模型与社会力模型结合，生成兼具物理合理性和语义一致性的移动轨迹。
- 在12人用户研究中，系统被评价为高效且易于上手。
- 适合城市规划、人流管理等需要模拟人类行为的场景使用。
语义轨迹分析通过捕捉访客画像、目标等语义信息，超越原始路径数据，揭示人们移动背后的动机。然而，真实场景中高质量数据收集成本高且语义信息匮乏，现有仿真工具又需较高技术门槛。为此，本文提出SenseWalk，一个基于大语言模型驱动的智能体交互式仿真系统。通过融合大模型与社会力模型，构建兼顾物理合理性与语义连贯性的仿真流程，并设计直观界面支持配置与结果分析。定量实验评估了仿真流程的有效性，用户研究（n=12）验证了系统的实用性和效率。
原文摘要 · Abstract (English)
Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through semantic information (e.g., visitors' profiles and goals) beyond raw spatial paths to better understand why people move in certain ways. However, analyzing semantic trajectories in real-world scenarios remains challenging, as collecting high-quality data is costly and often lacks rich semantic information. Meanwhile, existing simulation tools require substantial technical expertise, which makes them difficult for practitioners to adopt. To address these limitations, the paper proposes ${SenseWalk}$, an interactive system that supports simulating semantic trajectories by LLM-powered agents. We develop a simulation workflow that combines LLMs and the social force model to balance physical plausibility and semantic coherence. A user-friendly interface is designed to facilitate users in customizing the simulation configuration and analyzing simulation outputs. We also conduct a quantitative experiment to evaluate the effectiveness of our simulation workflow, and a user study (n=12) to assess the usefulness and efficiency of our system.
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