一种基于学习的基于服务水平契约和空闲车辆按需租赁的自动拼车平台优化方法

B. Beirigo, Frederik Schulte, R. Negenborn
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引用次数: 9

摘要

目前的移动出行服务在服务质量上无法与自有移动出行产品进行平等竞争。由于供需失衡,拼车用户总是会遇到延误、价格飙升和拒绝。传统方法往往不能充分应对需求波动,因为服务水平在某种程度上受到机队规模的限制。然而,随着自动驾驶汽车的出现,移动服务的特征发生了变化,并出现了克服当前限制的新机会。在本文中,我们考虑了一个自动拼车问题,其中空闲车辆被按需租用,以满足异构用户群的服务水平要求。面对需求不确定和车辆闲置的情况,我们提出了一种基于学习的优化方法,利用底层分配问题的双变量来迭代逼近不同可用性设置下每个时间和地点的车辆边际值。这些近似用于优化问题的目标函数中,以在纽约市曼哈顿的高分辨率交通网络中调度、再平衡和偶尔租用闲置的第三方车辆。结果表明,在使用较少车辆的情况下,提出的策略在各种车辆可用性场景下优于响应式优化方法。此外,我们证明了移动服务可以为不同的用户群体提供严格的服务水平合同,其中包括延迟和拒绝处罚。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Learning-Based Optimization Approach for Autonomous Ridesharing Platforms with Service-Level Contracts and On-Demand Hiring of Idle Vehicles
Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Because of supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately because service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles, however, the characteristics of mobility services change and new opportunities to overcome the prevailing limitations arise. In this paper, we consider an autonomous ridesharing problem in which idle vehicles are hired on-demand in order to meet the service-level requirements of a heterogeneous user base. In the face of uncertain demand and idle vehicle supply, we propose a learning-based optimization approach that uses the dual variables of the underlying assignment problem to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. These approximations are used in the objective function of the optimization problem to dispatch, rebalance, and occasionally hire idle third-party vehicles in a high-resolution transportation network of Manhattan, New York City. The results show that the proposed policy outperforms a reactive optimization approach in a variety of vehicle availability scenarios while hiring fewer vehicles. Moreover, we demonstrate that mobility services can offer strict service-level contracts to different user groups featuring both delay and rejection penalties.
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