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.

翻译：我们提出SF-LIFE，这是一个大规模模拟移动数据集，旨在加速交通、移动性和机器学习领域的研究。该数据集包含3,024,000,000,000条位置记录，完整记录了50万个模拟智能体在旧金山湾区路网中70天内以1Hz频率观测到的无噪声、多模态轨迹。数据捕捉：(1) 基于智能体生命模式仿真生成的需求驱动型个体日常活动计划；(2) 利用旧金山9个县40多家交通机构的开放街道地图数据，详细描述智能体在多个交通模式（包括公交、铁路、自行车、汽车和步行）下的运动轨迹。SF-LIFE基于真实交通基础设施（使用旧金山通用公交数据规范数据）和跨模态智能体运动，提供了前所未有的规模与细节。针对这一高保真模拟的旧金山表征，我们提供：(1) 带有交通模式标签的完整轨迹数据；(2) 时间频率降低的轨迹数据精简版；(3) 描述智能体访问地点因果活动的活动信息；(4) 智能体人口统计数据；(5) 底层OSM路网与建筑数据。作为首个具有如此规模与细节的数据集，SF-LIFE克服了真实世界追踪数据固有的隐私性、噪声和完整性问题，为交通优化、人类移动性分析和城市计算研究提供了稳健且符合伦理的数据资源。