用扩散模型模拟人一天到几周的出行行为，更真实还原城市生活。
Deep Generative Model for Human Mobility Behavior
- 基于行为与环境关联，用扩散模型生成多属性出行序列。
- 复现了地点访问、活动时间分配等关键规律，符合真实城市特征。
- 适合城市规划、公共卫生等需要精细移动行为分析的研究者。
理解与建模人类移动行为对交通规划、可持续城市设计和公共健康至关重要。尽管已有数十年研究，个体移动行为的模拟仍因复杂性、情境依赖性和探索性而困难。本文基于活动驱动的日常移动视角，提出MobilityGen——一种基于扩散的生成框架，可在大尺度空间上模拟数天至数周的多属性活动-出行序列。通过将行为属性与环境背景关联，MobilityGen复现了地点访问的标度律、活动时间分配及出行方式与目的地选择的耦合演化。模型体现时空变化性，生成多样化且合理的移动模式，与建成环境一致。除标准验证外，该模型还支持以往模型难以实现的分析，如不同出行方式对城市空间可达性的差异，以及共现动态如何影响社会接触与隔离。这些成果为人类移动行为及其社会影响的精细化研究提供了数据驱动的集成基础。
原文摘要 · Abstract (English)
Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite decades of effort, simulating individual mobility remains challenging because of its complex, context-dependent, and exploratory nature. Here, building on the activity-based view of daily mobility, we propose MobilityGen, a diffusion-based generative framework for simulating multi-attribute activity-travel sequences over days to weeks at large spatial scales. By linking behavioral attributes with environmental context, MobilityGen reproduces key patterns such as scaling laws for location visits, activity time allocation, and the coupled evolution of travel mode and destination choices. It reflects spatio-temporal variability and generates diverse and plausible mobility patterns consistent with the built environment. Beyond standard validation, MobilityGen enables analyses that have been difficult with earlier models, including how access to urban space varies across travel modes and how co-presence dynamics shape social exposure and segregation. Together, these results support an integrated, data-driven basis for fine-grained studies of human mobility behavior and its societal implications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。