Ridesharing services, such as Uber or Didi, have attracted considerable attention in recent years due to their positive impact on environmental protection and the economy. Existing studies require quick responses to orders, which lack the flexibility to accommodate longer wait times for better grouping opportunities. In this paper, we address a NP-hard ridesharing problem, called Minimal Extra Time RideSharing (METRS), which balances waiting time and group quality (i.e., detour time) to improve riders' satisfaction. To tackle this problem, we propose a novel approach called WATTER (WAit To be fasTER), which leverages an order pooling management algorithm allowing orders to wait until they can be matched with suitable groups. The key challenge is to customize the extra time threshold for each order by reducing the original optimization objective into a convex function of threshold, thus offering a theoretical guarantee to be optimized efficiently. We model the dispatch process using a Markov Decision Process (MDP) with a carefully designed value function to learn the threshold. Through extensive experiments on three real datasets, we demonstrate the efficiency and effectiveness of our proposed approaches.

翻译：共乘服务（如Uber或滴滴）因其对环境保护和经济的积极影响，近年来备受关注。现有研究要求对订单进行快速响应，但缺乏灵活容纳较长等待时间以获取更优分组机会的能力。本文研究了一个名为"最小额外时间共乘"（METRS）的NP难共乘问题，该问题通过平衡等待时间与组质量（即绕行时间）来提升乘客满意度。为解决此问题，我们提出了一种名为WATTER（Wait To Be fasTER）的创新方法，该方法利用订单池化管理算法，允许订单等待直至能与合适群组匹配。核心挑战在于通过将原始优化目标简化为关于阈值的凸函数，为每个订单定制额外时间阈值，从而提供可高效优化的理论保障。我们利用马尔可夫决策过程（MDP）对调度过程建模，并设计精细化的价值函数来学习该阈值。通过在三个真实数据集上的大量实验，我们验证了所提出方法的效率与有效性。