Plan-to-Synthesis：基于语义潜流匹配的跨城市人类移动生成

中文摘要 AI 辅助

人类移动生成旨在合成逼真的兴趣点（POI）访问轨迹，并已成为出行行为建模、交通管理和城市规划的重要工具。现有的基于扩散的方法虽然实现了高保真度，但由于地理空间位置和POI类别的固有异质性，需要逐城市生成，而基于大语言模型的方法虽然能够跨城市泛化，但在大规模应用中成本过高，尤其是对于长时域轨迹生成。为解决这一问题，我们提出了SeMoFlow，一种基于潜流匹配的语义人类移动生成框架。我们首先通过分层语义ID将不同城市的异构POI编码到共享的跨城市表示空间中，其中共享前缀捕获可迁移的语义，而后续编码逐步细化表示直至个体POI。基于语义ID，SeMoFlow遵循“规划到合成”的分层生成范式，其中自回归规划器生成粗粒度的语义和重复模式，流匹配实现器合成细粒度的后缀潜变量。生成的潜变量随后被解码并锚定到具体的POI。在大型多城市数据集上的大量实验表明，SeMoFlow实现了比现有基线更高的轨迹保真度，保留了城市特定的移动模式，并支持联合多城市生成和有效的跨城市迁移。

英文摘要

Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel behavior modeling, transportation management, and urban planning. Existing diffusion-based methods achieve high fidelity but require per-city generation, given the inherent heterogeneity of geospatial locations and POI categories, while large language model-based methods generalize across cities but remain too costly at scale, especially for long-horizon trajectory generation. To address this, we propose SeMoFlow, a Semantic human Mobility generation framework based on latent Flow matching. We first encode heterogeneous POIs from different cities into a shared cross-city representation space via hierarchical Semantic IDs, where shared prefixes capture transferable semantics, and successive codes progressively refine the representation toward individual POIs. Building on the semantic IDs, SeMoFlow follows a plan-to-synthesis hierarchical generation paradigm, in which an autoregressive planner generates coarse-grained semantic and recurrence patterns, and a flow matching realizer synthesizes fine-grained suffix latents. The generated latents are subsequently decoded and grounded to concrete POIs. Extensive experiments on large-scale multi-city datasets show that SeMoFlow achieves higher trajectory fidelity than existing baselines, preserves city-specific mobility motifs, and supports both joint multi-city generation and effective cross-city transfer.

发表机构

• Singapore-MIT Alliance for Research and Technology Centre, MIT（新加坡-麻省理工学院研究与技术联盟中心（麻省理工学院））
• Nanyang Technological University（南洋理工大学）
• Hong Kong University of Science & Technology (Guangzhou)（香港科技大学（广州））
• Korea Advanced Institute of Science & Technology（韩国高等科学技术研究院）
• University of Florida（佛罗里达大学）
• Massachusetts Institute of Technology（麻省理工学院）

机构由 AI 辅助整理，请以论文原文为准。