The emergence of the shared autonomous vehicle (SAV) provides new opportunities and challenges for the fashionable car-sharing mode. This study proposes a time-dependent SAV system design problem by jointly optimizing fleet size, parking infrastructure deployment, and daily operation of the system for infrastructure planning in the long run. The dynamic system optimum (DSO) principle in terms of total daily system cost (TDSC) is adopted to formulate the daily operation of the SAV system, i.e., users’ departure time choices and SAVs’ route choices. By incorporating the link transmission model (LTM) as the traffic flow model, the daily operation problem (DOP) of the SAV system is formulated as a linear programming (LP) problem. Further, the time-dependent SAV system design problem is formulated as a mixed integer linear programming (MILP) problem. The LP relaxation of the proposed MILP problem could provide a tight lower bound, and a diving heuristic algorithm is developed to solve the proposed MILP problem. Finally, numerical examples are designed to illustrate the properties of the model and the efficiency of the proposed solution algorithm.

共享自动驾驶汽车（SAV）的出现为时尚的汽车共享模式提供了新的机遇和挑战。这项研究通过共同优化车队规模，停车基础设施部署以及从长远来看系统的日常运行提出了一个随时间变化的SAV系统设计问题。采用动态系统优化（DSO）原理（基于每日总系统成本（TDSC））来制定SAV系统的日常运行，即用户的出发时间选择和SAV的路线选择。通过将链路传输模型（LTM）合并为交通流模型，SAV系统的日常运行问题（DOP）被表述为线性规划（LP）问题。此外，与时间有关的SAV系统设计问题被表述为混合整数线性规划（MILP）问题。提出的MILP问题的LP松弛可以提供一个紧密的下界，并且开发了一种潜水启发式算法来解决提出的MILP问题。最后，设计了数值例子来说明模型的性质和所提出的求解算法的效率。