Although highly valuable for a variety of applications, urban mobility data is rarely made openly available as it contains sensitive personal information. Synthetic data aims to solve this issue by generating artificial data that resembles an original dataset in structural and statistical characteristics, but omits sensitive information. For mobility data, a large number of corresponding models have been proposed in the last decade. This systematic review provides a structured comparative overview of the current state of this heterogeneous, active field of research. A special focus is put on the applicability of the reviewed models in practice.

翻译：尽管城市移动性数据在众多应用场景中具有极高价值，但由于包含敏感个人信息，这类数据很少被公开获取。合成数据旨在通过生成在结构和统计特征上与原始数据集相似、但剔除敏感信息的人工数据来解决这一问题。过去十年间，针对移动性数据已涌现大量相应模型。本系统性综述对这一异构且活跃的研究领域现状进行了结构化比较分析，特别关注所评述模型在实际应用中的适用性。