Understanding individual-level human mobility is critical for a wide range of applications. As such, real-world trajectory datasets provide valuable insights into actual movement behaviors and patterns of life but are often constrained by data sparsity and participant bias. Synthetic data, by contrast, offers scalability and flexibility but frequently lacks realism. To address this gap, we introduce a comprehensive software pipeline for, generating, calibrating, processing, and visualizing large-scale individual-level human mobility datasets that combine the realism of empirical data with the control and extensibility of Patterns-of-Life simulations. Our system consists of four integrated components. (1) a data generation engine which constructs geographically grounded simulations using OpenStreetMap data to produce diverse mobility logs. (2) a genetic algorithm-based calibration module that fine-tunes simulation parameters to align with real-world mobility characteristics, such as daily trip counts and radius of gyration, enabling realistic behavioral modeling. (3) a data processing suite which transforms raw simulation logs into structured formats suitable for downstream applications, including model training and benchmarking, and (4) a visualization module that extracts key mobility patterns and insights from the processed datasets and presents them through intuitive visual analytics for improved interpretability.

翻译：理解个体层面的人类移动对于广泛的应用至关重要。因此，现实世界的轨迹数据集为实际移动行为和生活模式提供了宝贵的见解，但常常受到数据稀疏性和参与者偏差的限制。相比之下，合成数据提供了可扩展性和灵活性，但往往缺乏真实性。为了弥补这一差距，我们引入了一个全面的软件流水线，用于生成、校准、处理和可视化大规模个体层面的人类移动数据集，该流水线结合了经验数据的真实性与生活模式仿真的可控性和可扩展性。我们的系统由四个集成组件组成：(1) 一个数据生成引擎，它使用OpenStreetMap数据构建基于地理的仿真，以生成多样化的移动日志。(2) 一个基于遗传算法的校准模块，用于微调仿真参数，以符合现实世界的移动特征，例如每日出行次数和回转半径，从而实现真实的行为建模。(3) 一个数据处理套件，将原始仿真日志转换为适合下游应用（包括模型训练和基准测试）的结构化格式，以及(4) 一个可视化模块，从处理后的数据集中提取关键的移动模式和见解，并通过直观的可视化分析进行呈现，以提高可解释性。