Carsharing is an integral part of the transformation toward flexible and sustainable mobility. New carsharing programs are entering the market to challenge large operators by offering innovative services. This study investigates the use of generative machine learning models for creating synthetic data to support carsharing decision–making when data access is limited. To this end, it explores the evaluation, selection, and implementation of leading-edge methods, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate synthetic tabular transaction data of carsharing trips. The study analyzes usage data of an emerging carsharing program that is expanding its services to include free-floating electric vehicles (EVs). The results show that augmenting real training data with synthetic samples improves predictive modeling of upcoming trips by up to 4.63%. These results support carsharing researchers and practitioners in generating and leveraging synthetic mobility data to develop solutions to real-world decision support problems in carsharing.

汽车共享是向灵活和可持续交通转型的一个组成部分。新的汽车共享项目正在进入市场，通过提供创新服务来挑战大型运营商。本研究调查了使用生成机器学习模型来创建合成数据，以在数据访问受限时支持汽车共享决策。为此，它探索了前沿方法的评估、选择和实施，例如生成对抗网络（GAN）和变分自动编码器（VAE），以生成汽车共享行程的综合表格交易数据。该研究分析了一项新兴汽车共享计划的使用数据，该计划正在将其服务扩展到包括自由浮动电动汽车 (EV)。结果表明，使用合成样本增强真实训练数据可将即将到来的行程的预测模型提高高达 4.63%。这些结果支持汽车共享研究人员和从业者生成和利用综合移动数据来开发汽车共享中现实世界决策支持问题的解决方案。