Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this question, a joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP) is introduced, and a bi-level mathematical programming formulation and solution approach are presented. The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable and allowing the removal of bus routes. The lower-level problem consists of a dynamic combined mode choice-traveler assignment problem (DCMC-TAP) formulation. The heuristic solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based assignment-simulation approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the region’s existing multimodal transit network. The computational results indicate significant traveler benefits, in terms of improved average traveler wait times, associated with optimizing the joint design of multimodal transit networks and SAMS fleets compared with the initial transit network design.

为低密度地区的旅行者（尤其是没有私家车的旅行者）提供优质的运输服务，对于运输公司来说是一个持续的挑战。全自动驾驶汽车（AVs）的出现以及将其纳入机动性服务车队中，可能会使公交机构提供更好的服务和/或减少其自有资金和运营成本。这项研究的重点是在大城市地区的交通方式与运营（或补贴）共享使用的AV移动服务（SAMS）之间分配资源的问题。为了解决这个问题，引入了联合公交网络的重新设计和SAMS机队规模确定问题（JTNR-SFSDP），并提出了一种双层数学规划公式和解决方法。上级问题通过将SAMS车队规模纳入决策变量并允许删除公交路线来修改公交网络频率设置问题（TNFSP）公式。下层问题由动态组合模式选择旅行者分配问题（DCMC-TAP）公式组成。启发式求解过程包括使用非线性编程求解器求解上级问题，并使用基于迭代代理的分配模拟方法求解下级问题。为了说明建模框架的有效性，本研究使用了芝加哥的旅行者需求以及该地区现有的多式联运网络。计算结果表明，从改善的平均旅行者等待时间，