我们提出 SF-LIFE，一个大规模模拟移动数据集，旨在加速交通、移动性与机器学习领域的研究。该数据集包含 3.024 万亿条位置记录，以 1 Hz 频率完整、无噪声地刻画了 50 万名模拟智能体在旧金山湾区路网中为期 70 天的多模态轨迹。数据涵盖：（1）基于人类生活模式的智能体级基于需求的日常行程安排，由智能体仿真模型生成；（2）基于 OpenStreetMap 表示的旧金山路网、整合来自 9 个县逾 40 家公共交通机构数据所生成的精细运动学轨迹。SF-LIFE 在规模与细节上均属前所未有：其轨迹建模依托真实的公共交通基础设施，采用旧金山通用公共交通数据规范（GTFS）数据，并涵盖公交、轨道、自行车、机动车及步行等多种交通方式。针对这一高保真度的旧金山模拟表征，我们提供：（1）标注有交通方式标签的完整轨迹数据；（2）时间采样频率降低的轻量版轨迹数据；（3）描述智能体访问某地之因果活动的活动信息；（4）智能体人口统计学数据；（5）底层 OpenStreetMap 路网与建筑数据。作为首个达到此规模与细节水平的数据集，SF-LIFE 克服了真实世界追踪数据固有的隐私性、噪声与完整性限制，为公共交通优化、人类移动性分析及城市计算等研究提供了稳健且符合伦理规范的数据资源。

We introduce SF-LIFE, a large-scale simulated movement dataset designed to accelerate research in transportation, mobility, and machine learning. The dataset contains 3,024,000,000,000 location records capturing complete, noise-free, multi-modality trajectories of 500,000 simulated agents observed at a 1Hz frequency navigating the San Francisco Bay Area network over a 70-day period. The data captures (1) needs-driven daily agendas of individual agents generated by an agent-based simulation of human patterns of life and (2) detailed kinematic trajectories moving agents across the OpenStreetMap representation of San Francisco using data from 40+ transit agencies across 9 counties. SF-LIFE provides unprecedented scale and detail as trajectories are based on real transit infrastructure using San Francisco General Transit Feed Specification (GTFS) data, having agent movements across multiple modalities, including bus, rail, bike, automobile, and walking. For this high-fidelity simulated representation of San Francisco, we provide (1) the full trajectory data annotated with transportation mode labels, (2) reduced-size versions of the trajectory data with reduced temporal frequency, (3) agent activity information describing the causal activity why an agent visits a place, (4) agent demographic data, and (5) the underlying OSM road network and building data. As the first dataset of its scale and level of detail, SF-LIFE overcomes the privacy, noise, and completeness limitations inherent in real-world tracking data, providing a robust and ethically sourced resource for research in transit optimization, human mobility analysis, and urban computing.