We introduce KaRRi, an improved algorithm for scheduling a fleet of shared vehicles as it is used by services like UberXShare and Lyft Shared. We speed up the basic online algorithm that looks for all possible insertions of a new customer into a set of existing routes, we generalize the objective function, and efficiently support a large number of possible pick-up and drop-off locations. This lays an algorithmic foundation for ridesharing systems with higher vehicle occupancy -- enabling greatly reduced cost and ecological impact at comparable service quality. We find that our algorithm computes assignments between vehicles and riders several times faster than a previous state-of-the-art approach. Further, we observe that allowing meeting points for vehicles and riders can reduce the operating cost of vehicle fleets by up to $15\%$ while also reducing passenger wait and trip times.

翻译：我们提出KaRRi算法，这是一种用于调度共享车队（如UberXShare和Lyft Shared等服务所用）的改进型算法。通过加速基础在线算法（该算法用于检测新乘客插入现有路线集合的所有可能方案），我们泛化了目标函数，并高效支持大量可选的上车与下车地点。这为高载客率的拼车系统奠定了算法基础——在保持相当服务质量的同时，大幅降低运营成本与生态影响。实验表明，我们的算法在车辆与乘客之间的分配速度比现有最优方法快数倍。此外，我们发现允许车辆与乘客设置会合点，可在降低乘客等待时间与行程时长的同时，将车队运营成本降低高达15%。