用公开数据生成家庭协同出行，让无数据地区也能建模真实移动行为。
Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs
- 基于检索增强的LLM，仅用公开统计信息生成带家庭协作的出行链
- 在NHTS和SCAG-ABM数据集上实现有效移动模式合成
- 适合缺乏高质量出行数据的区域建模与交通规划研究
理解人类移动模式一直是交通建模中的难题。由于跨地区高质量训练数据获取困难，传统基于活动的模型与学习型移动建模算法严重受限于数据可用性与质量。现有方法主要关注时空模式，忽视了活动间的语义关系（如逻辑关联或依赖）以及家庭协同活动（如共同购物、家庭聚餐）等关键因素，这些对真实移动建模至关重要。本文提出一种检索增强的大语言模型框架，仅利用公开可得的统计与社会人口学信息，生成包含家庭协调活动的出行链，降低对复杂出行数据的依赖。检索增强机制确保了家庭协调行为的一致性，并维持生成模式的统计合理性，填补了现有方法的关键空白。在NHTS与SCAG-ABM数据集上的验证表明，该方法能有效合成移动模式，且对缺乏出行数据的区域具有强适应性。
原文摘要 · Abstract (English)
Understanding human mobility patterns has long been a challenging task in transportation modeling. Due to the difficulties in obtaining high-quality training datasets across diverse locations, conventional activity-based models and learning-based human mobility modeling algorithms are particularly limited by the availability and quality of datasets. Current approaches primarily focus on spatial-temporal patterns while neglecting semantic relationships such as logical connections or dependencies between activities and household coordination activities like joint shopping trips or family meal times, both crucial for realistic mobility modeling. We propose a retrieval-augmented large language model (LLM) framework that generates activity chains with household coordination using only public accessible statistical and socio-demographic information, reducing the need for sophisticated mobility data. The retrieval-augmentation mechanism enables household coordination and maintains statistical consistency across generated patterns, addressing a key gap in existing methods. Our validation with NHTS and SCAG-ABM datasets demonstrates effective mobility synthesis and strong adaptability for regions with limited mobility data availability.
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