Understanding individual-level human mobility is critical for a wide range of applications. Real-world trajectory datasets provide valuable insights into actual movement behaviors but are often constrained by data sparsity and participant bias. Synthetic data, by contrast, offer scalability and flexibility but frequently lack realism. To address this gap, we introduce a comprehensive software pipeline for calibrating, generating, 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 constructs geographically grounded simulations using OpenStreetMap data to produce diverse mobility logs. (2) a genetic algorithm-based calibration module 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 transforms raw simulation logs into structured formats suitable for downstream applications, including model training and benchmarking. (4) a visualization module 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行为对于众多应用至关重要。真实世界的轨迹数据集为实际移动行为提供了宝贵见解，但常受数据稀疏性和参与者偏差的限制。相比之下，合成数据虽具有可扩展性和灵活性，却往往缺乏真实性。为弥补这一差距，我们引入了一个综合软件流程，用于校准、生成、处理和可视化大规模个体层面的人类移动数据集，该流程结合了经验数据的真实性与生活模式仿真的可控性及可扩展性。我们的系统包含四个集成组件：(1) 数据生成引擎利用OpenStreetMap数据构建地理基础仿真，以生成多样化的移动日志；(2) 基于遗传算法的校准模块微调仿真参数，使其与真实世界移动特征（如每日出行次数和回转半径）保持一致，从而实现真实的行为建模；(3) 数据处理套件将原始仿真日志转换为适用于下游应用（包括模型训练和基准测试）的结构化格式；(4) 可视化模块从处理后的数据集中提取关键移动模式和见解，并通过直观的可视化分析进行呈现，以提升可解释性。