网约车的需求估计和机会约束车队管理
Demand Estimation and Chance-Constrained Fleet Management for Ride Hailing
摘要：
在自动按需移动（MOD）系统中，客户请求从共享车辆车队乘坐，这些共享车辆可以响应于客户需求自动定位。MOD系统的最新方法集中在客户只能通过应用程序或在车站等待请求乘车的环境中。本文开发了MOD车队管理方法，用于乘车，客户可以通过简单地呼叫路过的车辆来请求乘车，这种方法对校园MOD系统特别重要。叫车服务面临的挑战是，客户需求并没有像应用程序那样明确提供，而是只有在车辆恰好位于到达地点时才能为客户提供服务。这项工作的重点是通过学习和利用客户需求，最大限度地提高服务在MOD系统中的招呼客户的数量。贝叶斯框架是用来定义一个新的客户需求模型，它结合了观察到的行人交通估计客户到达的位置与量化的不确定性。提出了一种探索规划器，其路由MOD车辆，以减少到达率的不确定性。提出了一种鲁棒的叫车车队管理规划器，该规划器使用机会约束公式在存在不确定性的情况下对车辆进行路由。在麻省理工学院的校园模拟一个真实世界的MOD系统证明了规划者的有效性。客户需求模型和探索计划器被证明可以随着时间的推移减少估计误差，并且乘车计划器可以将系统中服务客户的比例提高73%，超过基线探索方法。
英文摘要：
In autonomous Mobility on Demand (MOD) systems, customers request rides from a fleet of shared vehicles that can be automatically positioned in response to customer demand. Recent approaches to MOD systems have focused on environments where customers can only request rides through an app or by waiting at a station. This paper develops MOD fleet management approaches for ride hailing, where customers may instead request rides simply by hailing a passing vehicle, an approach of particular importance for campus MOD systems. The challenge for ride hailing is that customer demand is not explicitly provided as it would be with an app, but rather customers are only served if a vehicle happens to be located at the arrival location. This work focuses on maximizing the number of served hailing customers in an MOD system by learning and utilizing customer demand. A Bayesian framework is used to define a novel customer demand model which incorporates observed pedestrian traffic to estimate customer arrival locations with a quantification of uncertainty. An exploration planner is proposed which routes MOD vehicles in order to reduce arrival rate uncertainty. A robust ride hailing fleet management planner is proposed which routes vehicles under the presence of uncertainty using a chance-constrained formulation. Simulation of a real-world MOD system on MIT's campus demonstrates the effectiveness of the planners. The customer demand model and exploration planner are demonstrated to reduce estimation error over time and the ride hailing planner is shown to improve the fraction of served customers in the system by 73\% over a baseline exploration approach.
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。
