Predictive positioning and quality of service ridesharing for campus mobility on demand systems
Predictive positioning and quality of service ridesharing for campus mobility on demand systems
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校园移动按需系统的预测定位和服务质量共乘
DOI：
10.1109/icra.2017.7989167
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
J. How
中科院分区：
文献类型：
--
作者：
Justin Miller;J. How
自动随需移动（MOD）系统可以利用车队管理策略来提供高客户服务质量（QoS）。先前在自主MOD系统上的工作已经开发了重新平衡单容量车辆的方法，其中通过大型车队规模来维持QoS。这项工作的重点是使用少量车辆的MOD系统，例如校园内的车辆，由于乘车需求的增加，无法引入额外的车辆。提出了一种预测定位方法，通过识别关键位置来定位车队，从而提高客户的服务质量，从而最大限度地减少客户的预期等待时间。随着到达率的增加，引入拼车作为提高客户QoS的一种手段。然而，对于拼车来说，感知到的QoS依赖于通常未知的客户偏好。为了应对这一挑战，开发了一个客户评级模型，该模型可以从5星评级中学习客户偏好，并直接整合到拼车算法中。将预测定位和拼车方法应用于现实校园MOD系统的仿真。预测定位和拼车相结合的方法可将客户服务时间缩短29%。客户评级模型显示，在一系列客户偏好中，国防部车队管理表现最佳。
Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies in order to provide a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS by identifying key locations to position the fleet in order to minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system. A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29%. and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.