The combination of autonomous vehicles (AVs) and free-floating car-sharing scheme is expected to deliver high potentials of both through effective AV self-relocations. Little research has been done on the deployment of shared AVs (SAVs) considering the interplays among SAV relocations, supply-demand dynamics, and travelers’ multi-modal multi-activity schedules. This study aims to propose a bi-level system optimal model inclusive of a new hub-based relocation strategy to moderate the supply and demand of SAVs. The lower-level captures travelers’ activity-travel scheduling behavior by an extended dynamic user equilibrium model and the upper-level determines the hub locations, fleet size, and initial distribution of SAVs. A heuristic algorithm based on Lagrangian relaxation is developed to solve the network design problem. Numerical examples demonstrate that SAV relocations can significantly influence travelers’ daily schedules and enhance mobility efficiency in the multi-modal transport system. We also find that the proposed hub-based relocation strategy outperforms two common SAV relocation strategies in the literature.

自动驾驶汽车和自由浮动的汽车共享计划相结合，有望通过有效的自动驾驶汽车自动重定位实现两者的巨大潜力。考虑到SAV搬迁，供需动态和旅行者的多模式多活动时间表之间的相互作用，关于共享AV（SAV）的部署的研究很少。这项研究旨在提出一个双层系统优化模型，其中包括基于枢纽的新搬迁策略，以缓解SAV的供求关系。下层通过扩展的动态用户平衡模型捕获旅客的活动行程调度行为，上层确定枢纽位置，机队规模和SAV的初始分布。提出了一种基于拉格朗日松弛的启发式算法来解决网络设计问题。数值示例表明，SAV的重新布置可以显着影响旅行者的日常行程，并提高多式联运系统中的出行效率。我们还发现，提出的基于集线器的重定位策略优于文献中的两种常见的SAV重定位策略。