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, we advance a unified event-level formulation of daily mobility and propose MobilityGen to generate multi-attribute event 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.

翻译：理解和建模人类移动行为是交通规划、可持续城市设计和公共卫生等领域核心挑战的关键。尽管经过数十年的努力，由于个体移动行为具有复杂、情境依赖和探索性等特征，其模拟仍然面临挑战。本文提出了一种统一的事件级日常移动行为建模框架，并提出了MobilityGen模型，用于在大空间尺度上生成数日至数周的多属性事件序列。通过将行为属性与环境情境相联结，MobilityGen能够复现关键行为模式，例如地点访问的标度律、活动时间分配，以及出行方式与目的地选择的耦合演化过程。该模型反映了时空变异性，并能生成与建成环境相一致的多样化、合理移动模式。除标准验证外，MobilityGen还支持以往模型难以实现的分析，包括不同出行方式下城市空间可达性的差异，以及共现动态如何塑造社会暴露与隔离模式。这些研究成果共同为细粒度研究人类移动行为及其社会影响提供了集成化、数据驱动的理论基础。