用大模型生成个人出行日记，数据来自公开的普查与土地利用信息。
Generating Individual Travel Diaries Using Large Language Models Informed by Census and Land-Use Data
- 基于美国普查和土地数据库，用大模型随机生成人物画像并直接生成出行日记。
- 生成日记在出行目的判断上更准确，整体真实度达0.485，接近传统方法的0.455。
- 提出四维综合评分体系，可量化评估合成日记的真实性，适合城市规划研究者使用。
本研究提出一种基于大语言模型（LLM）的个体出行日记生成方法，用于基于代理的交通模型。传统方法依赖大量私有家庭出行调查数据，而本文从开源的美国社区调查（ACS）和智能选址数据库（SLD）中随机生成人物画像，并通过直接提示生成出行日记。研究引入一种新型的“一人一队列”真实性评分：由四个指标（行程数量、间隔、目的、方式）构成的综合得分，经康涅狄格州全州交通研究（CSTS）日记验证，匹配人口统计变量。采用Jensen-Shannon散度衡量生成日记与真实日记的分布相似性。与经典方法（负二项式生成行程数；多项对数回归生成方式/目的）相比，经验证集校准后，LLM生成日记的整体真实性得分（0.485）与经典方法（0.455）相当。LLM在行程目的识别上表现更优，且评分分布更集中（一致性更高），而经典模型在行程数量和活动持续时间的数值估计上略胜一筹。聚合验证显示LLM具有更好的统计代表性（均值0.612对比0.435），证明了其零样本可行性，并为未来合成日记评估系统建立了可量化的现实性指标。
原文摘要 · Abstract (English)
This study introduces a Large Language Model (LLM) scheme for generating individual travel diaries in agent-based transportation models. While traditional approaches rely on large quantities of proprietary household travel surveys, the method presented in this study generates personas stochastically from open-source American Community Survey (ACS) and Smart Location Database (SLD) data, then synthesizes diaries through direct prompting. This study features a novel one-to-cohort realism score: a composite of four metrics (Trip Count Score, Interval Score, Purpose Score, and Mode Score) validated against the Connecticut Statewide Transportation Study (CSTS) diaries, matched across demographic variables. The validation utilizes Jensen-Shannon Divergence to measure distributional similarities between generated and real diaries. When compared to diaries generated with classical methods (Negative Binomial for trip generation; Multinomial Logit for mode/purpose) calibrated on the validation set, LLM-generated diaries achieve comparable overall realism (LLM mean: 0.485 vs. 0.455). The LLM excels in determining trip purpose and demonstrates greater consistency (narrower realism score distribution), while classical models lead in numerical estimates of trip count and activity duration. Aggregate validation confirms the LLM's statistical representativeness (LLM mean: 0.612 vs. 0.435), demonstrating LLM's zero-shot viability and establishing a quantifiable metric of diary realism for future synthetic diary evaluation systems.
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