Shared mobility has an important role in supporting existing transportation options in cities. However, when not deployed carefully, shared services may have operational inefficiencies such as low occupancies and increased deadheading. One reason is the spatio-temporal variance in the distribution of urban trip demand, which may lead to an unbalanced fleet displaced in cities thus unable to serve requested trips. Strategically siting holding areas (depots for dispatching and relocating fleets) could help improve fleet performance. Therefore, this paper considers shared autonomous vehicle (SAV) fleet operations by modeling the impacts of different holding area policies on service performance. Modeling and comparing multiple holding area policies for tactically deploying SAVs is novel, and the insights from this paper can inform service providers on how to site holding areas for improved performance. We develop a model of SAV fleet with pooling in the City of Toronto, with 27,951 total SAV trip requests across a 16-h period. We then integrate four holding area policies estimated using different spatial clustering methods, centralized positioning, and existing taxi stands. Findings indicate that using agglomerative clustering results in superior SAV fleet performances (average passenger waiting times reduced by about 20% compared with the worst performing policy), with increased served demand and reduced deadheading. A single holding area at a high trip density location yields efficient service performance at lower fleets but struggles to serve sparse demand (producing worst results); this method may suffice for operating SAV services within a small geofence with high trip densities.

共享出行在支持城市现有交通选择方面发挥着重要作用。但是，如果部署不仔细，共享服务可能会出现运营效率低下的情况，例如占用率低和空头增加。原因之一是城市出行需求分布的时空差异，这可能导致不平衡的车队在城市中流离失所，从而无法满足要求的出行。战略性地选址等待区（调度和重新安置车队的仓库）可以帮助提高车队绩效。因此，本文通过对不同保留区政策对服务性能的影响进行建模来考虑共享自动驾驶汽车 (SAV) 车队的运营。为战术部署 SAV 建模和比较多个控制区策略是新颖的，并且本文中的见解可以告知服务提供商如何定位待命区域以提高性能。我们开发了一个在多伦多市汇集的 SAV 车队模型，在 16 小时内总共有 27,951 次 SAV 旅行请求。然后，我们整合了使用不同空间聚类方法、集中定位和现有出租车站估计的四种停车区政策。调查结果表明，使用凝聚聚类可实现卓越的 SAV 车队性能（与性能最差的策略相比，平均乘客等待时间减少了约 20%），增加了服务需求并减少了空驶。在高出行密度位置的单个等待区在较低的车队中产生高效的服务性能，但难以满足稀疏的需求（产生最差的结果）；