自动驾驶出租车应在哪里运营？面向自主出行即服务的战略网络设计
Where Should Robotaxis Operate? Strategic Network Design for Autonomous Mobility-on-Demand
AI总结：
本文提出了一种面向自主出行即服务的战略网络设计方法，通过混合整数规划和列生成算法，解决服务网络与车队容量的联合设计问题，并展示了其在实际数据中的应用效果。
摘要：
自动驾驶出行即服务（AMoD）服务的出现为提高按需出行系统的效率和可靠性创造了新机遇。与由人类驾驶的出行即服务（MoD）不同，AMoD能够实现完全集中的车队控制，但同时也需要适当的基础设施，使得车辆只能在适当仪器化的道路子网络上安全运行。大多数现有的AMoD研究集中在固定道路网络上的车队控制（匹配、再平衡、拼车）上，并未解决服务网络和车队容量的联合设计问题。在本文中，我们将这一战略设计问题正式定义为自主出行即服务网络设计问题（AMoD-NDP），其中运营商选择一个操作子网络并调度所有乘客，受基础设施和车队约束以及路线级服务质量要求的限制。我们提出了一种基于路径的混合整数公式来描述AMoD-NDP，并开发了一种基于列生成的算法，能够扩展到城市级网络。主问题在受限路径集合上进行优化，而定价问题简化为带有资源约束的最短路径问题，通过定制的标签修正算法精确求解。该方法提供了最优性间隙的显式证明，并可自然扩展到旅行时间和需求存在箱不确定性下的鲁棒对应问题。使用纽约市曼哈顿的真实数据，我们展示了该框架能够产生稳定且可解释的操作子网络，量化基础设施投资与车队时间之间的权衡，并能够容纳额外的路径级约束，如左转限制作为操作风险的代理。这些结果展示了所提出方法如何支持未来AMoD部署的战略规划和政策分析。
英文摘要：
The emergence of Autonomous Mobility-on-Demand (AMoD) services creates new opportunities to improve the efficiency and reliability of on-demand mobility systems. Unlike human-driven Mobility-on-Demand (MoD), AMoD enables fully centralized fleet control, but it also requires appropriate infrastructure, so that vehicles can operate safely only on a suitably instrumented subnetwork of the roads. Most existing AMoD research focuses on fleet control (matching, rebalancing, ridepooling) on a fixed road network and does not address the joint design of the service network and fleet capacity. In this paper, we formalize this strategic design problem as the Autonomous Mobility-on-Demand Network Design Problem (AMoD-NDP), in which an operator selects an operation subnetwork and routes all passengers, subject to infrastructure and fleet constraints and route-level quality-of-service requirements. We propose a path-based mixed-integer formulation of the AMoD-NDP and develop a column-generation-based algorithm that scales to city-sized networks. The master problem optimizes over a restricted set of paths, while the pricing problem reduces to an elementary shortest path with resource constraints, solved exactly by a tailored label-correcting algorithm. The method provides an explicit certificate of the optimality gap and extends naturally to a robust counterpart under box uncertainty in travel times and demand. Using real-world data from Manhattan, New York City, we show that the framework produces stable and interpretable operation subnetworks, quantifies trade-offs between infrastructure investment and fleet time, and accommodates additional path-level constraints, such as limits on left turns as a proxy for operational risk. These results illustrate how the proposed approach can support strategic planning and policy analysis for future AMoD deployments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
