  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.
